morph_FINAL_new
Allison Davis Connelly
12/22/2023
Morphological variation of amazon and sailfin mollies
In this study, I am looking at various populations of amazon and sailfin mollies across their native/introduced range to assess morphological variation both within and among the species. I am using the Pickle fish collections for my samples.
All data
I will use all data first to see the trends, then filter for confirmed adult sizes (see “Filtered”).
Initial steps
Data collection
## Using libcurl 8.3.0 with SchannelChecking normality
Checking normality
I will use Shapiro-wilke, histograms, and QQ plots to determine what traits are normal. These will only be performed on continuous variables, as discrete variables are not normal by nature.
Conclusions: literally all of them are NOT normal… will log transform them and run parametric tests (t-test, F-test, ANOVA). Not sure what to do with the discrete variables…[Logan said to use non-parametric OR a glmer with poisson distribution]
SW Test
shapiro.test(raw1$SL)##
## Shapiro-Wilk normality test
##
## data: raw1$SL
## W = 0.9763, p-value = 2.763e-05shapiro.test(raw1$BD)##
## Shapiro-Wilk normality test
##
## data: raw1$BD
## W = 0.96287, p-value = 1.787e-07shapiro.test(raw1$CPD)##
## Shapiro-Wilk normality test
##
## data: raw1$CPD
## W = 0.96431, p-value = 2.908e-07shapiro.test(raw1$CPL)##
## Shapiro-Wilk normality test
##
## data: raw1$CPL
## W = 0.97288, p-value = 6.831e-06shapiro.test(raw1$PreDL)##
## Shapiro-Wilk normality test
##
## data: raw1$PreDL
## W = 0.97924, p-value = 9.997e-05shapiro.test(raw1$DbL)##
## Shapiro-Wilk normality test
##
## data: raw1$DbL
## W = 0.97697, p-value = 3.68e-05shapiro.test(raw1$HL)##
## Shapiro-Wilk normality test
##
## data: raw1$HL
## W = 0.94955, p-value = 3.057e-09shapiro.test(raw1$HD)##
## Shapiro-Wilk normality test
##
## data: raw1$HD
## W = 0.96761, p-value = 9.28e-07shapiro.test(raw1$HW)##
## Shapiro-Wilk normality test
##
## data: raw1$HW
## W = 0.97201, p-value = 4.833e-06shapiro.test(raw1$SnL)##
## Shapiro-Wilk normality test
##
## data: raw1$SnL
## W = 0.65042, p-value < 2.2e-16shapiro.test(raw1$OL)##
## Shapiro-Wilk normality test
##
## data: raw1$OL
## W = 0.98631, p-value = 0.003102Histograms
hist(raw1$SL)
hist(raw1$BD)
hist(raw1$CPD)
hist(raw1$CPL)
hist(raw1$PreDL)
hist(raw1$DbL)
hist(raw1$HL)
hist(raw1$HD)
hist(raw1$HW)
hist(raw1$SnL)
hist(raw1$OL)
QQ plots
qqnorm(raw1$SL)
qqline(raw1$SL)
qqnorm(raw1$BD)
qqline(raw1$BD)
qqnorm(raw1$CPD)
qqline(raw1$CPD)
qqnorm(raw1$CPL)
qqline(raw1$CPL)
qqnorm(raw1$PreDL)
qqline(raw1$PreDL)
qqnorm(raw1$DbL)
qqline(raw1$DbL)
qqnorm(raw1$HL)
qqline(raw1$HL)
qqnorm(raw1$HD)
qqline(raw1$HD)
qqnorm(raw1$HW)
qqline(raw1$HW)
qqnorm(raw1$SnL)
qqline(raw1$SnL)
qqnorm(raw1$OL)
qqline(raw1$OL)
Log transformations
Since all of the continuous characters were not normal, I will log transform them, and proceed with the transformed data in all analyses.
raw1[paste0(names(raw1), '_log')] <- log(raw1[, 26:35], 10)
#for some reason did the log of the whole data frame instead of those specific columns, so I will just remove the unnecessary columns instead of spending time on what went wrong.
raw1 <- raw1[-c(37:60)]
raw1 <- raw1[-c(48)]
#double checking normality with a SW test and QQ plot
shapiro.test(raw1$SL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$SL_log
## W = 0.99271, p-value = 0.1056shapiro.test(raw1$BD_log)##
## Shapiro-Wilk normality test
##
## data: raw1$BD_log
## W = 0.82598, p-value < 2.2e-16shapiro.test(raw1$CPD_log)##
## Shapiro-Wilk normality test
##
## data: raw1$CPD_log
## W = 0.99285, p-value = 0.1144shapiro.test(raw1$CPL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$CPL_log
## W = 0.99513, p-value = 0.3824shapiro.test(raw1$PreDL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$PreDL_log
## W = 0.93554, p-value = 8.204e-11shapiro.test(raw1$DbL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$DbL_log
## W = 0.98042, p-value = 0.0001711shapiro.test(raw1$HL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$HL_log
## W = 0.99386, p-value = 0.1984shapiro.test(raw1$HD_log)##
## Shapiro-Wilk normality test
##
## data: raw1$HD_log
## W = 0.99661, p-value = 0.7102shapiro.test(raw1$HW_log)##
## Shapiro-Wilk normality test
##
## data: raw1$HW_log
## W = 0.99491, p-value = 0.3435shapiro.test(raw1$SnL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$SnL_log
## W = 0.99366, p-value = 0.1788shapiro.test(raw1$OL_log)##
## Shapiro-Wilk normality test
##
## data: raw1$OL_log
## W = 0.99271, p-value = 0.1056qqnorm(raw1$SL_log)
qqline(raw1$SL_log)
qqnorm(raw1$BD_log)
qqline(raw1$BD_log)
qqnorm(raw1$CPD_log)
qqline(raw1$CPD_log)
qqnorm(raw1$CPL_log)
qqline(raw1$CPL_log)
qqnorm(raw1$PreDL_log)
qqline(raw1$PreDL_log)
qqnorm(raw1$DbL_log)
qqline(raw1$DbL_log)
qqnorm(raw1$HL_log)
qqline(raw1$HL_log)
qqnorm(raw1$HD_log)
qqline(raw1$HD_log)
qqnorm(raw1$HW_log)
qqline(raw1$HW_log)
qqnorm(raw1$SnL_log)
qqline(raw1$SnL_log)
qqnorm(raw1$OL_log)
qqline(raw1$OL_log)
Correcting for body size (residuals)
SL correction
Since amazons are in general bigger than sailfin, we don’t want any results to be due to this difference in body size bias. Therefore, we will see what traits are influenced by body size (regressions) and correct for body size when necessary (absolute value of residuals). We can then use the residuals when comparing between species for traits that are influenced by body size, and raw/transformed data for traits that are not influenced by body size. I will also calculate standardized residuals to compare residuals across traits in later analyses.
Quick results summary: traits not influenced by body size are left & right pelvic, anal, scales above and below lateral line (except in mexicana), scales before dorsal fin and fluctuating asymmetry; all other traits influenced by body size.
Regressions
- STEP ONE: plot trait vs body size
library(ggplot2)
library(ggpubr)
lat <- raw1[raw1$SPP == "p.latipinna",]
form <- raw1[raw1$SPP == "p.formosa",]
mex <- raw1[raw1$SPP == "p.mexicana",]
##### LAT #####
reg.lat.D <- lm(lat$D ~ lat$SL)
sd.lat.D <- rstandard(reg.lat.D)
reg.lat.D.plot <- ggplot(lat, aes(x = SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.D.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.P1 <- lm(lat$P1 ~ lat$SL)
sd.lat.P1 <- rstandard(reg.lat.P1)
reg.lat.P1.plot <- ggplot(lat, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.P1.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.P2.L <- lm(lat$P2.L ~ lat$SL)
sd.lat.P2.L <- rstandard(reg.lat.P2.L)
reg.lat.P2.L.plot <- ggplot(lat, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.P2.R <- lm(lat$P2.R ~ lat$SL)
sd.lat.P2.R <- rstandard(reg.lat.P2.R)
reg.lat.P2.R.plot <- ggplot(lat, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.A <- lm(lat$A ~ lat$SL)
sd.lat.A <- rstandard(reg.lat.A)
reg.lat.A.plot <- ggplot(lat, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.A.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.P1.R <- lm(lat$P1.R ~ lat$SL)
sd.lat.P1.R <- rstandard(reg.lat.P1.R)
reg.lat.P1.R.plot <- ggplot(lat, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.LLSC <- lm(lat$LLSC ~ lat$SL)
sd.lat.LLSC <- rstandard(reg.lat.LLSC)
reg.lat.LLSC.plot <- ggplot(lat, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.SALL <- lm(lat$SALL ~ lat$SL)
sd.lat.SALL <- rstandard(reg.lat.SALL)
reg.lat.SALL.plot <- ggplot(lat, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.SBLL <- lm(lat$SBLL ~ lat$SL)
sd.lat.SBLL <- rstandard(reg.lat.SBLL)
reg.lat.SBLL.plot <- ggplot(lat, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.SBDF <- lm(lat$SBDF ~ lat$SL)
sd.lat.SBDF <- rstandard(reg.lat.SBDF)
reg.lat.SBDF.plot <- ggplot(lat, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.BD <- lm(lat$BD_log ~ lat$SL_log)
sd.lat.BD <- rstandard(reg.lat.BD)
reg.lat.BD.plot <- ggplot(lat, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.BD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.CPD <- lm(lat$CPD_log ~ lat$SL_log)
sd.lat.CPD <- rstandard(reg.lat.CPD)
reg.lat.CPD.plot <- ggplot(lat, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.CPL <- lm(lat$CPL_log ~ lat$SL_log)
sd.lat.CPL <- rstandard(reg.lat.CPL)
reg.lat.CPL.plot <- ggplot(lat, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.PreDL <- lm(lat$PreDL_log ~ lat$SL_log)
sd.lat.PreDL <- rstandard(reg.lat.PreDL)
reg.lat.PreDL.plot <- ggplot(lat, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.DbL <- lm(lat$DbL_log ~ lat$SL_log)
sd.lat.DbL <- rstandard(reg.lat.DbL)
reg.lat.DbL.plot <- ggplot(lat, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.HL <- lm(lat$HL_log ~ lat$SL_log)
sd.lat.HL <- rstandard(reg.lat.HL)
reg.lat.HL.plot <- ggplot(lat, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.HL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.HD <- lm(lat$HD_log ~ lat$SL_log)
sd.lat.HD <- rstandard(reg.lat.HD)
reg.lat.HD.plot <- ggplot(lat, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.HD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.HW <- lm(lat$HW_log ~ lat$SL_log)
sd.lat.HW <- rstandard(reg.lat.HW)
reg.lat.HW.plot <- ggplot(lat, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.HW.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.SnL <- lm(lat$SnL_log ~ lat$SL_log)
sd.lat.SnL <- rstandard(reg.lat.SnL)
reg.lat.SnL.plot <- ggplot(lat, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.OL <- lm(lat$OL_log ~ lat$SL_log)
sd.lat.OL <- rstandard(reg.lat.OL)
reg.lat.OL.plot <- ggplot(lat, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.OL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.lat.FLA <- lm(lat$FLA ~ lat$SL)
sd.lat.FLA <- rstandard(reg.lat.FLA)
reg.lat.FLA.plot <- ggplot(lat, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.lat.FLA.plot## `geom_smooth()` using formula = 'y ~ x'
##### FORM #####
reg.form.D <- lm(form$D ~ form$SL)
sd.form.D <- rstandard(reg.form.D)
reg.form.D.plot <- ggplot(form, aes(x =SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.D.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.P1 <- lm(form$P1 ~ form$SL)
sd.form.P1 <- rstandard(reg.form.P1)
reg.form.P1.plot <- ggplot(form, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.P1.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.P2.L <- lm(form$P2.L ~ form$SL)
sd.form.P2.L <- rstandard(reg.form.P2.L)
reg.form.P2.L.plot <- ggplot(form, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.P2.R <- lm(form$P2.R ~ form$SL)
sd.form.P2.R <- rstandard(reg.form.P2.R)
reg.form.P2.R.plot <- ggplot(form, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.A <- lm(form$A ~ form$SL)
sd.form.A <- rstandard(reg.form.A)
reg.form.A.plot <- ggplot(form, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.A.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.P1.R <- lm(form$P1.R ~ form$SL)
sd.form.P1.R <- rstandard(reg.form.P1.R)
reg.form.P1.R.plot <- ggplot(form, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.LLSC <- lm(form$LLSC ~ form$SL)
sd.form.LLSC <- rstandard(reg.form.LLSC)
reg.form.LLSC.plot <- ggplot(form, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.SALL <- lm(form$SALL ~ form$SL)
sd.form.SALL <- rstandard(reg.form.SALL)
reg.form.SALL.plot <- ggplot(form, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.SBLL <- lm(form$SBLL ~ form$SL)
sd.form.SBLL <- rstandard(reg.form.SBLL)
reg.form.SBLL.plot <- ggplot(form, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.SBDF <- lm(form$SBDF ~ form$SL)
sd.form.SBDF <- rstandard(reg.form.SBDF)
reg.form.SBDF.plot <- ggplot(form, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.BD <- lm(form$BD_log ~ form$SL_log)
sd.form.BD <- rstandard(reg.form.BD)
reg.form.BD.plot <- ggplot(form, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.BD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.CPD <- lm(form$CPD_log ~ form$SL_log)
sd.form.CPD <- rstandard(reg.form.CPD)
reg.form.CPD.plot <- ggplot(form, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.CPL <- lm(form$CPL_log ~ form$SL_log)
sd.form.CPL <- rstandard(reg.form.CPL)
reg.form.CPL.plot <- ggplot(form, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.PreDL <- lm(form$PreDL_log ~ form$SL_log)
sd.form.PreDL <- rstandard(reg.form.PreDL)
reg.form.PreDL.plot <- ggplot(form, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.DbL <- lm(form$DbL_log ~ form$SL_log)
sd.form.DbL <- rstandard(reg.form.DbL)
reg.form.DbL.plot <- ggplot(form, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.HL <- lm(form$HL_log ~ form$SL_log)
sd.form.HL <- rstandard(reg.form.HL)
reg.form.HL.plot <- ggplot(form, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.HL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.HD <- lm(form$HD_log ~ form$SL_log)
sd.form.HD <- rstandard(reg.form.HD)
reg.form.HD.plot <- ggplot(form, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.HD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.HW <- lm(form$HW_log ~ form$SL_log)
sd.form.HW <- rstandard(reg.form.HW)
reg.form.HW.plot <- ggplot(form, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.HW.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.SnL <- lm(form$SnL_log ~ form$SL_log)
sd.form.SnL <- rstandard(reg.form.SnL)
reg.form.SnL.plot <- ggplot(form, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.OL <- lm(form$OL_log ~ form$SL_log)
sd.form.OL <- rstandard(reg.form.OL)
reg.form.OL.plot <- ggplot(form, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.OL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.form.FLA <- lm(form$FLA ~ form$SL)
sd.form.FLA <- rstandard(reg.form.FLA)
reg.form.FLA.plot <- ggplot(form, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.form.FLA.plot## `geom_smooth()` using formula = 'y ~ x'
##### MEX #####
reg.mex.D <- lm(mex$D ~ mex$SL)
sd.mex.D <- rstandard(reg.mex.D)
reg.mex.D.plot <- ggplot(mex, aes(x = SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.D.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.P1 <- lm(mex$P1 ~ mex$SL)
sd.mex.P1 <- rstandard(reg.mex.P1)
reg.mex.P1.plot <- ggplot(mex, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.P1.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.P2.L <- lm(mex$P2.L ~ mex$SL)
sd.mex.P2.L <- rstandard(reg.mex.P2.L)
reg.mex.P2.L.plot <- ggplot(mex, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.P2.R <- lm(mex$P2.R ~ mex$SL)
sd.mex.P2.R <- rstandard(reg.mex.P2.R)
reg.mex.P2.R.plot <- ggplot(mex, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.A <- lm(mex$A ~ mex$SL)
sd.mex.A <- rstandard(reg.mex.A)
reg.mex.A.plot <- ggplot(mex, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.A.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.P1.R <- lm(mex$P1.R ~ mex$SL)
sd.mex.P1.R <- rstandard(reg.mex.P1.R)
reg.mex.P1.R.plot <- ggplot(mex, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.LLSC <- lm(mex$LLSC ~ mex$SL)
sd.mex.LLSC <- rstandard(reg.mex.LLSC)
reg.mex.LLSC.plot <- ggplot(mex, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.SALL <- lm(mex$SALL ~ mex$SL)
sd.mex.SALL <- rstandard(reg.mex.SALL)
reg.mex.SALL.plot <- ggplot(mex, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.SBLL <- lm(mex$SBLL ~ mex$SL)
sd.mex.SBLL <- rstandard(reg.mex.SBLL)
reg.mex.SBLL.plot <- ggplot(mex, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.SBDF <- lm(mex$SBDF ~ mex$SL)
sd.mex.SBDF <- rstandard(reg.mex.SBDF)
reg.mex.SBDF.plot <- ggplot(mex, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.BD <- lm(mex$BD_log ~ mex$SL_log)
sd.mex.BD <- rstandard(reg.mex.BD)
reg.mex.BD.plot <- ggplot(mex, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.BD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.CPD <- lm(mex$CPD_log ~ mex$SL_log)
sd.mex.CPD <- rstandard(reg.mex.CPD)
reg.mex.CPD.plot <- ggplot(mex, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.CPL <- lm(mex$CPL_log ~ mex$SL_log)
sd.mex.CPL <- rstandard(reg.mex.CPL)
reg.mex.CPL.plot <- ggplot(mex, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.PreDL <- lm(mex$PreDL_log ~ mex$SL_log)
sd.mex.PreDL <- rstandard(reg.mex.PreDL)
reg.mex.PreDL.plot <- ggplot(mex, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.DbL <- lm(mex$DbL_log ~ mex$SL_log)
sd.mex.DbL <- rstandard(reg.mex.DbL)
reg.mex.DbL.plot <- ggplot(mex, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.HL <- lm(mex$HL_log ~ mex$SL_log)
sd.mex.HL <- rstandard(reg.mex.HL)
reg.mex.HL.plot <- ggplot(mex, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.HL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.HD <- lm(mex$HD_log ~ mex$SL_log)
sd.mex.HD <- rstandard(reg.mex.HD)
reg.mex.HD.plot <- ggplot(mex, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.HD.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.HW <- lm(mex$HW_log ~ mex$SL_log)
sd.mex.HW <- rstandard(reg.mex.HW)
reg.mex.HW.plot <- ggplot(mex, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.HW.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.SnL <- lm(mex$SnL_log ~ mex$SL_log)
sd.mex.SnL <- rstandard(reg.mex.SnL)
reg.mex.SnL.plot <- ggplot(mex, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.OL <- lm(mex$OL_log ~ mex$SL_log)
sd.mex.OL <- rstandard(reg.mex.OL)
reg.mex.OL.plot <- ggplot(mex, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.OL.plot## `geom_smooth()` using formula = 'y ~ x'
reg.mex.FLA <- lm(mex$FLA ~ mex$SL)
sd.mex.FLA <- rstandard(reg.mex.FLA)
reg.mex.FLA.plot <- ggplot(mex, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
reg.mex.FLA.plot## `geom_smooth()` using formula = 'y ~ x'
Residuals
STEP TWO: get residuals for each individual for traits that were influenced by body size
STEP THREE: convert residuals to absolute value
##### LAT #####
abs.lat.D <- abs(res.lat.D)
mean(abs.lat.D)## [1] 0.5375505abs.lat.P1 <- abs(res.lat.P1)
mean(abs.lat.P1)## [1] 0.5466667abs.lat.P1.R <- abs(res.lat.P1.R)
mean(abs.lat.P1.R)## [1] 0.5799393abs.lat.LLSC <- abs(res.lat.LLSC)
mean(abs.lat.LLSC)## [1] 0.7664044abs.lat.SBLL <- abs(res.lat.SBLL)
mean(abs.lat.SBLL)## [1] 0.2461336abs.lat.BD <- abs(res.lat.BD)
mean(abs.lat.BD)## [1] 0.03636357abs.lat.CPD <- abs(res.lat.CPD)
mean(abs.lat.CPD)## [1] 0.03695739abs.lat.CPL <- abs(res.lat.CPL)
mean(abs.lat.CPL)## [1] 0.03732998abs.lat.PreDL <- abs(res.lat.PreDL)
mean(abs.lat.PreDL)## [1] 0.04810736abs.lat.DbL <- abs(res.lat.DbL)
mean(abs.lat.DbL)## [1] 0.04126962abs.lat.HL <- abs(res.lat.HL)
mean(abs.lat.HL)## [1] 0.03588147abs.lat.HD <- abs(res.lat.HD)
mean(abs.lat.HD)## [1] 0.03506907abs.lat.HW <- abs(res.lat.HW)
mean(abs.lat.HW)## [1] 0.03208151abs.lat.SnL <- abs(res.lat.SnL)
mean(abs.lat.SnL)## [1] 0.03208694abs.lat.OL <- abs(res.lat.OL)
mean(abs.lat.OL)## [1] 1.416081e-17##### FORM #####
abs.form.D <- abs(res.form.D)
mean(abs.form.D)## [1] 0.5668177abs.form.P1 <- abs(res.form.P1)
mean(abs.form.P1)## [1] 0.4843616abs.form.P1.R <- abs(res.form.P1.R)
mean(abs.form.P1.R)## [1] 0.4242033abs.form.LLSC <- abs(res.form.LLSC)
mean(abs.form.LLSC)## [1] 0.9038801abs.form.SBLL <- abs(res.form.SBLL)
mean(abs.form.SBLL)## [1] 0.3399272abs.form.BD <- abs(res.form.BD)
mean(abs.form.BD)## [1] 0.04191086abs.form.CPD <- abs(res.form.CPD)
mean(abs.form.CPD)## [1] 0.03046568abs.form.CPL <- abs(res.form.CPL)
mean(abs.form.CPL)## [1] 0.03738523abs.form.PreDL <- abs(res.form.PreDL)
mean(abs.form.PreDL)## [1] 0.05207252abs.form.DbL <- abs(res.form.DbL)
mean(abs.form.DbL)## [1] 0.03261267abs.form.HL <- abs(res.form.HL)
mean(abs.form.HL)## [1] 0.03919057abs.form.HD <- abs(res.form.HD)
mean(abs.form.HD)## [1] 0.03507274abs.form.HW <- abs(res.form.HW)
mean(abs.form.HW)## [1] 0.03744209abs.form.SnL <- abs(res.form.SnL)
mean(abs.form.SnL)## [1] 0.03321741abs.form.OL <- abs(res.form.OL)
mean(abs.form.OL)## [1] 6.018764e-18##### MEX #####
abs.mex.D <- abs(res.mex.D)
mean(abs.mex.D)## [1] 0.1657275abs.mex.P1 <- abs(res.mex.P1)
mean(abs.mex.P1)## [1] 0.5686425abs.mex.P1.R <- abs(res.mex.P1.R)
mean(abs.mex.P1.R)## [1] 0.458723abs.mex.LLSC <- abs(res.mex.LLSC)
mean(abs.mex.LLSC)## [1] 0.4434954abs.mex.SBLL <- abs(res.mex.SBLL)
mean(abs.mex.SBLL)## [1] 0.2713231abs.mex.BD <- abs(res.mex.BD)
mean(abs.mex.BD)## [1] 0.03300381abs.mex.CPD <- abs(res.mex.CPD)
mean(abs.mex.CPD)## [1] 0.03277006abs.mex.CPL <- abs(res.mex.CPL)
mean(abs.mex.CPL)## [1] 0.03878753abs.mex.PreDL <- abs(res.mex.PreDL)
mean(abs.mex.PreDL)## [1] 0.03943373abs.mex.DbL <- abs(res.mex.DbL)
mean(abs.mex.DbL)## [1] 0.02950105abs.mex.HL <- abs(res.mex.HL)
mean(abs.mex.HL)## [1] 0.05562721abs.mex.HD <- abs(res.mex.HD)
mean(abs.mex.HD)## [1] 0.03668815abs.mex.HW <- abs(res.mex.HW)
mean(abs.mex.HW)## [1] 0.03882769abs.mex.SnL <- abs(res.mex.SnL)
mean(abs.mex.SnL)## [1] 0.04900589abs.mex.OL <- abs(res.mex.OL)
mean(abs.mex.OL)## [1] 4.001035e-18#let's get this into the raw1 data set so that we can plot this more easily
abs.res.D <- c(abs.lat.D, abs.form.D, abs.mex.D)
abs.res.P1 <- c(abs.lat.P1, abs.form.P1, abs.mex.P1)
abs.res.P1.R <- c(abs.lat.P1.R, abs.form.P1.R, abs.mex.P1.R)
abs.res.LLSC<- c(abs.lat.LLSC, abs.form.LLSC, abs.mex.LLSC)
abs.res.SBLL<- c(abs.lat.SBLL, abs.form.SBLL, abs.mex.SBLL)
abs.res.BD<- c(abs.lat.BD, abs.form.BD, abs.mex.BD)
abs.res.CPD<- c(abs.lat.CPD, abs.form.CPD, abs.mex.CPD)
abs.res.CPL<- c(abs.lat.CPL, abs.form.CPL, abs.mex.CPL)
abs.res.PreDL <- c(abs.lat.PreDL, abs.form.PreDL, abs.mex.PreDL)
abs.res.DbL <- c(abs.lat.DbL, abs.form.DbL, abs.mex.DbL)
abs.res.HL<- c(abs.lat.HL, abs.form.HL, abs.mex.HL)
abs.res.HD<- c(abs.lat.HD, abs.form.HD, abs.mex.HD)
abs.res.HW <- c(abs.lat.HW, abs.form.HW, abs.mex.HW)
abs.res.SnL <- c(abs.lat.SnL, abs.form.SnL, abs.mex.SnL)
abs.res.OL <- c(abs.lat.OL, abs.form.OL, abs.mex.OL)
raw2 <- cbind(raw1, abs.res.D, abs.res.P1, abs.res.P1.R, abs.res.LLSC, abs.res.SBLL, abs.res.BD, abs.res.CPD, abs.res.CPL, abs.res.PreDL, abs.res.DbL, abs.res.HL, abs.res.HD, abs.res.HW, abs.res.SnL, abs.res.OL)Residual Comparisons
- STEP FOUR: plot the mean of the |residuals| for both species, for all traits influenced by body size
library(ggbeeswarm)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionggplot(raw2, aes(SPP, abs.res.D)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
scatter_violin <- ggplot(data=raw2, aes(x=SPP, y=abs.res.D)) +
geom_violin(trim = FALSE) +
stat_summary(
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "pointrange", color = "black"
)
print(scatter_violin)
scatter_violin1 <- ggplot(data=raw2, aes(x=SPP, y=abs.res.D)) +
geom_violin(trim = FALSE) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="crossbar", fill="red", width=0.03)
print(scatter_violin1)
ggplot(raw2, aes(SPP, abs.res.P1)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.P1.R)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3) 
ggplot(raw2, aes(SPP, abs.res.LLSC)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.SBLL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.BD)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.CPD)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.CPL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3) 
ggplot(raw2, aes(SPP, abs.res.PreDL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.DbL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.HL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.HD)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.HW)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.SnL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
ggplot(raw2, aes(SPP, abs.res.OL)) +
geom_point(alpha=0.3) +
stat_summary(fun.data=function(x){mean_cl_normal(x, conf.int=.683)}, geom="errorbar",
width=0.03, colour="red", alpha=0.7) +
stat_summary(fun=mean, geom="point", fill="red", pch=21, size=3)
Comparing variation
Variance tests
t-test for residuals, continuous f-test for raw, continuous all discrete will go through glmer/non parametric with either residuals or raw
(note: did non-parametric instead of transforming, but Mike said to transform, so I copied that work to the ‘morphology-analysis-final’ Rmd).
F-test on residuals doesn’t make much sense; the residuals themselves are representative of the variation present, as they are the distance from the mean. Therefore, F-test on residuals is like variance of the variance. Instead, I have to do a t-test on the absolute value of the residuals. In this sense, we want to see if the mean of the absolute residuals is higher or lower for asexual species–is the average amount of variation higher or lower for this trait? Based on the regressions, if the trait was influenced by body size, I will perform a t-test on the absolute value of the residuals. If the trait was not influenced by body size, I will perform an F-test of variance on the raw data.
Quick results summary: For the F-test on raw data, none of the traits were significantly different (P2L/R, A, SBDF, FLA). For the t-tests on residuals, the only significant traits are left pectoral (), right pectoral (lat>form), scales above lateral line (), scales below lateral line (form>lat), and head length ().
- will do two-tailed and check out the residual means to infer direction; for traits in which we use raw data, a one-tailed f-test will be perfomed in both direction to determine which species is varying more. We will also visulize the variation using a histogram to confirm direction results.F tests to compare species
ALL OF THESE ARE DISCRETE SO I DON’T THINK I DO ANY F TESTS
This will only be for the traits that did not have a significant regression compared to SL (so P2s, A, SALL, SBDF, and FLA); none of these traits were transformed, so just doing raw for now.
var.left.pel <- var.test(lat\(P2.L, form\)P2.L, alternative = “two.sided”) var.left.pel
var.rig.pel <- var.test(lat\(P2.R, form\)P2.R, alternative = “two.sided”) var.rig.pel
var.anal <- var.test(lat\(A, form\)A, alternative = “two.sided”) var.anal
var.sca.ab.ll <- var.test(lat\(SALL, form\)SALL, alternative = “two.sided”) var.sca.ab.ll
var.sca.df <- var.test(lat\(SBDF, form\)SBDF, alternative = “two.sided”) var.sca.df
var.flu.asy <- var.test(lat\(FLA, form\)FLA, alternative = “two.sided”) var.flu.asy
T tests to compare species
CONTINUOUS RESIDUALS
- will do two-tailed and check out the means to infer direction (see beginning for trait means w/o body correction)
- this will be for the traits that did have a significant regression compared to SL; will be using the absolute value residuals.
- For continuous traits, we will be using the log transformed data.
##
## Welch Two Sample t-test
##
## data: abs.lat.BD and abs.form.BD
## t = -0.83822, df = 215.6, p-value = 0.4028
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.018591485 0.007496913
## sample estimates:
## mean of x mean of y
## 0.03636357 0.04191086##
## Welch Two Sample t-test
##
## data: abs.lat.CPD and abs.form.CPD
## t = 2.1468, df = 282.5, p-value = 0.03266
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0005394989 0.0124439196
## sample estimates:
## mean of x mean of y
## 0.03695739 0.03046568##
## Welch Two Sample t-test
##
## data: abs.lat.CPL and abs.form.CPL
## t = -0.015652, df = 291.31, p-value = 0.9875
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.007002590 0.006892088
## sample estimates:
## mean of x mean of y
## 0.03732998 0.03738523##
## Welch Two Sample t-test
##
## data: abs.lat.PreDL and abs.form.PreDL
## t = -0.67807, df = 215.55, p-value = 0.4985
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.015491199 0.007560893
## sample estimates:
## mean of x mean of y
## 0.04810736 0.05207252##
## Welch Two Sample t-test
##
## data: abs.lat.DbL and abs.form.DbL
## t = 2.3458, df = 281.07, p-value = 0.01968
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.00139255 0.01592134
## sample estimates:
## mean of x mean of y
## 0.04126962 0.03261267##
## Welch Two Sample t-test
##
## data: abs.lat.HL and abs.form.HL
## t = -0.90175, df = 296.34, p-value = 0.3679
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.010530987 0.003912795
## sample estimates:
## mean of x mean of y
## 0.03588147 0.03919057##
## Welch Two Sample t-test
##
## data: abs.lat.HD and abs.form.HD
## t = -0.00099329, df = 267.38, p-value = 0.9992
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.007275651 0.007268313
## sample estimates:
## mean of x mean of y
## 0.03506907 0.03507274##
## Welch Two Sample t-test
##
## data: abs.lat.HW and abs.form.HW
## t = -1.6871, df = 297.98, p-value = 0.09263
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0116134871 0.0008923157
## sample estimates:
## mean of x mean of y
## 0.03208151 0.03744209##
## Welch Two Sample t-test
##
## data: abs.lat.SnL and abs.form.SnL
## t = -0.38886, df = 295.99, p-value = 0.6977
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006851803 0.004590862
## sample estimates:
## mean of x mean of y
## 0.03208694 0.03321741##
## Welch Two Sample t-test
##
## data: abs.lat.OL and abs.form.OL
## t = 1.318, df = 152.51, p-value = 0.1895
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.062607e-18 2.034670e-17
## sample estimates:
## mean of x mean of y
## 1.416081e-17 6.018764e-18Levene’s test
Since the count data can’t do parametric, I will do non-parametric tests. Logan mentioned a glmer with poisson distribution but I don’t think this will answer the question of a difference in variance between the species. The linear model will instead tell me how much species effects differences in the trait?? I guess?
I will use Levene’s test as a non-parametric F-test, to check out differences in variance in raw data.
I will use Mann Whitney U test as a non-parametric t-test, to check out differences in variance in residual data.
Still only performing this on the traits that did NOT vary with SL (P2L/R, A, SALL, SBLL [between sail and amz only], SBDF, FLA).
raw3 <- raw2[raw2$SPP !="p.mexicana", ]
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodelibrary(carData)
(LT_P2L <- leveneTest(P2.L~SPP, data=raw3)) #gives nothing since it's all the same value## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 NaN NaN
## 298(LT_P2R <- leveneTest(P2.R~SPP, data=raw3)) #same as above## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 NaN NaN
## 298(LT_A <- leveneTest(A~SPP, data=raw3)) ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.3962 0.5295
## 298(LT_SALL <- leveneTest(SALL~SPP, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.8627 0.3537
## 298(LT_SBLL <- leveneTest(SBLL~SPP, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 3.36 0.0678 .
## 298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(LT_SBDF <- leveneTest(SBDF~SPP, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.0081 0.9283
## 298(LT_FLA <- leveneTest(FLA~SPP, data=raw3))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 2.7164 0.1004
## 298Mann Whitney U tests
This will be performed on traits that DID vary with SL.
(MW_D <- wilcox.test(abs.res.D~SPP, data=raw3, conf.int=T))##
## Wilcoxon rank sum test with continuity correction
##
## data: abs.res.D by SPP
## W = 12186, p-value = 0.1545
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.05689062 0.19840357
## sample estimates:
## difference in location
## 0.1030553(MW_P1 <- wilcox.test(abs.res.P1~SPP, data=raw3, conf.int=T))##
## Wilcoxon rank sum test with continuity correction
##
## data: abs.res.P1 by SPP
## W = 8765, p-value = 0.001606
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.23328614 -0.07245187
## sample estimates:
## difference in location
## -0.1618195(MW_P1R <- wilcox.test(abs.res.P1.R~SPP, data=raw3, conf.int=T))##
## Wilcoxon rank sum test with continuity correction
##
## data: abs.res.P1.R by SPP
## W = 7363, p-value = 4.86e-07
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2596445 -0.1543936
## sample estimates:
## difference in location
## -0.2082393(MW_LLSC <- wilcox.test(abs.res.LLSC~SPP, data=raw3, conf.int=T))##
## Wilcoxon rank sum test with continuity correction
##
## data: abs.res.LLSC by SPP
## W = 12119, p-value = 0.1822
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.03418245 0.19226705
## sample estimates:
## difference in location
## 0.0763304Summary of variance results
average.table <- matrix(c(MW_D\(p.value, mean(abs.lat.D, na.rm = TRUE), mean(abs.form.D, na.rm = TRUE), MW_P1\)p.value, mean(abs.lat.P1, na.rm = TRUE), mean(abs.form.P1, na.rm = TRUE), LT_P2R\("Pr(>F)", mean(lat\)P2.R, na.rm = TRUE), mean(form\(P2.R, na.rm = TRUE), LT_P2R\)“Pr(>F)”, mean(lat\(P2.L, na.rm = TRUE), mean(form\)P2.L, na.rm = TRUE), LT_A\("Pr(>F)", mean(lat\)A, na.rm = TRUE), mean(form\(A, na.rm = TRUE), MW_P1R\)p.value, mean(abs.lat.P1.R, na.rm = TRUE), mean(abs.form.P1.R, na.rm = TRUE), MW_LLSC\(p.value, mean(abs.lat.LLSC, na.rm = TRUE), mean(abs.form.LLSC, na.rm = TRUE), LT_SALL\)“Pr(>F)”, mean(lat\(SALL, na.rm = TRUE), mean(form\)SALL, na.rm = TRUE), LT_SBLL\("Pr(>F)", mean(abs.lat.SBLL, na.rm = TRUE), mean(abs.form.SBLL, na.rm = TRUE), LT_SBDF\)“Pr(>F)”, mean(lat\(SBDF, na.rm = TRUE), mean(form\)SBDF, na.rm = TRUE), ttest.abs.BD\(p.value, mean(abs.lat.BD, na.rm = TRUE), mean(abs.form.BD, na.rm = TRUE), ttest.abs.CPD\)p.value, mean(abs.lat.CPD, na.rm = TRUE), mean(abs.form.CPD, na.rm = TRUE), ttest.abs.CPL\(p.value, mean(abs.lat.CPL, na.rm = TRUE), mean(abs.form.CPL, na.rm = TRUE), ttest.abs.PreDL\)p.value, mean(abs.lat.PreDL, na.rm = TRUE), mean(abs.form.PreDL, na.rm = TRUE), ttest.abs.DbL\(p.value, mean(abs.lat.DbL, na.rm = TRUE), mean(abs.form.DbL, na.rm = TRUE), ttest.abs.HL\)p.value, mean(abs.lat.HL, na.rm = TRUE), mean(abs.form.HL, na.rm = TRUE), ttest.abs.HD\(p.value, mean(abs.lat.HD, na.rm = TRUE), mean(abs.form.HD, na.rm = TRUE), ttest.abs.HW\)p.value, mean(abs.lat.HW, na.rm = TRUE), mean(abs.form.HW, na.rm = TRUE), ttest.abs.SnL\(p.value, mean(abs.lat.SnL, na.rm = TRUE), mean(abs.form.SnL, na.rm = TRUE), ttest.abs.OL\)p.value, mean(abs.lat.OL, na.rm = TRUE), mean(abs.form.OL, na.rm = TRUE), LT_FLA\("Pr(>F)", mean(lat\)FLA, na.rm = TRUE), mean(form$FLA, na.rm = TRUE)), ncol=3, byrow=TRUE)
colnames(average.table)<- c(“p-value for variance”, “lat mean”, “form mean”)
rownames(average.table) <- c(“dorsal rays”, “L pect rays”, “R pelvic rays”, ”L pelvic rays”, “Anal rays”, “R pect rays”, ”lat line scales”, ”scales above LL”, ”scales below LL”, ”scales before dor”, “body dep”, “peduncle dep”, “peduncle leng”, “pre-dorsal”, “dorsal base”, “head length”, “head depth”, “head width”, “snout leng”, “orbital leng”, “fluct. asymmetries”)
average.table
Variance plots
plot_multi_histogram <- function(df, feature, label_column) {
plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
geom_histogram(bins=10, alpha=0.4, position="identity", aes(y = ..density..), color="black") +
geom_density(alpha=0.2) +
labs(x=feature, y = "Density")
plt + guides(fill=guide_legend(title=label_column))
}
plot_multi_histogram(raw3, "abs.res.CPD", "SPP")## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot_multi_histogram(raw3, "abs.res.DbL", "SPP")
plot_multi_histogram(raw3, "abs.res.P1", "SPP")
plot_multi_histogram(raw3, "abs.res.P1.R", "SPP")
ANOVA
Will run an ANOVA on the residuals with location and species as fixed effects. This will show me if morphology depends on the species, the location, and if the location and species interact to determine morphology. CANNOT DO WITH COUNT DATA–may have to run a glmer with zero-tuncated negative binomial/quasi-poisson distribution instead (think this will tell me if species, location, and interaction affect the trait…). Will only work with the residuals, because comparing the means of the residuals indicates differences in variation. Would I not just use Levene’s and MWU again but include spp and zone/basin/watershed?
Leaving the anovas for count data in rn until I figure out what to do.
I will first run this using the zones as the location factor. Zones (1-4) represent the latitude range with equivalent sample sizes in each, since the collections were not equally representative of all latitudes, and I wanted to avoid a sampling bias when randomly selecting samples. Zone 1 corresponds to the southern most latitude range, and zone 4 corresponds to the northern most latitude range.
I will then run the same analysis using basin as the location factor. Since fish are physically isolated to the river basins they occupy, the genetic variation is also limited to that basin. Thus it is possible for fish within the same basin to be more similar due to genetic/physical constraints. (will also do with watershed just to see).
Lastly I will run ANOVAs with both zones and basins but with standardized residuals. This would allow me to compare overall variation across traits (at least those that are depended on body size) rather than just one trait at a time. Not 100% sure if this is useful (or correct to do), but thought it would be interesting.
ANOVAs
Amazon
Zones
library(ggplot2)
lat3 <- raw2[raw2$SPP == "p.latipinna", ]
form3 <- raw2[raw2$SPP == "p.latipinna", ]
A.D <- aov(abs.res.D ~ QUARTILE, data=form3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.306 0.7685 4.292 0.00635 **
## Residuals 130 23.277 0.1791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.D)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ QUARTILE, data=form3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.159 0.05296 0.444 0.722
## Residuals 130 15.511 0.11932A.P1.R <- aov(abs.res.P1.R ~ QUARTILE, data=form3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.065 0.02172 0.166 0.919
## Residuals 130 16.987 0.13067ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.P1.R)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ QUARTILE, data=form3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.31 0.4354 1.209 0.309
## Residuals 130 46.80 0.3600A.SBLL <- aov(abs.res.SBLL ~ QUARTILE, data=form3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.436 0.1454 1.142 0.335
## Residuals 130 16.546 0.1273ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.SBLL)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ QUARTILE, data=form3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00259 0.0008622 1.058 0.369
## Residuals 130 0.10591 0.0008147ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.BD)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ QUARTILE, data=form3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01249 0.004163 6.832 0.00026 ***
## Residuals 130 0.07922 0.000609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.CPD)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ QUARTILE, data=form3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.02074 0.006912 9.448 1.08e-05 ***
## Residuals 130 0.09511 0.000732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.CPL)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ QUARTILE, data=form3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.0082 0.002742 0.79 0.502
## Residuals 130 0.4511 0.003470ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.PreDL)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ QUARTILE, data=form3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.0195 0.006501 7.136 0.000179 ***
## Residuals 130 0.1184 0.000911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.DbL)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ QUARTILE, data=form3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00344 0.0011466 1.324 0.269
## Residuals 130 0.11260 0.0008661ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.HL)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ QUARTILE, data=form3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00672 0.002239 2.04 0.111
## Residuals 130 0.14269 0.001098A.HW <- aov(abs.res.HW ~ QUARTILE, data=form3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01136 0.003788 7.212 0.000163 ***
## Residuals 130 0.06827 0.000525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.HW)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ QUARTILE, data=form3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00766 0.0025545 5.052 0.00242 **
## Residuals 130 0.06573 0.0005056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ QUARTILE, data=form3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.17e-32 7.242e-33 1.538 0.208
## Residuals 130 6.12e-31 4.708e-33ggplot(form3, aes(x=factor(QUARTILE), y=abs.res.OL)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ BASIN, data=form3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 2.789 0.5579 3.133 0.0106 *
## Residuals 128 22.793 0.1781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.D)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ BASIN, data=form3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.945 0.1891 1.643 0.153
## Residuals 128 14.725 0.1150A1.P1.R <- aov(abs.res.P1.R ~ BASIN, data=form3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.159 0.03187 0.241 0.943
## Residuals 128 16.893 0.13197ggplot(form3, aes(x=factor(BASIN), y=abs.res.P1.R)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ BASIN, data=form3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 4.54 0.9080 2.668 0.025 *
## Residuals 128 43.57 0.3404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.LLSC)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ BASIN, data=form3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.755 0.1510 1.191 0.317
## Residuals 128 16.228 0.1268ggplot(form3, aes(x=factor(BASIN), y=abs.res.SBLL)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ BASIN, data=form3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00316 0.0006313 0.767 0.575
## Residuals 128 0.10534 0.0008230ggplot(form3, aes(x=factor(BASIN), y=abs.res.BD)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ BASIN, data=form3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01713 0.003425 5.879 6.34e-05 ***
## Residuals 128 0.07458 0.000583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.CPD)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ BASIN, data=form3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.02013 0.004025 5.383 0.000159 ***
## Residuals 128 0.09572 0.000748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ BASIN, data=form3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.0222 0.004437 1.299 0.268
## Residuals 128 0.4372 0.003415ggplot(form3, aes(x=factor(BASIN), y=abs.res.PreDL)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ BASIN, data=form3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.03427 0.006855 8.464 6.05e-07 ***
## Residuals 128 0.10367 0.000810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.DbL)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ BASIN, data=form3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00766 0.0015321 1.809 0.115
## Residuals 128 0.10838 0.0008467ggplot(form3, aes(x=factor(BASIN), y=abs.res.HL)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ BASIN, data=form3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00684 0.001367 1.228 0.3
## Residuals 128 0.14257 0.001114ggplot(form3, aes(x=factor(BASIN), y=abs.res.HD)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ BASIN, data=form3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01460 0.0029194 5.746 8.11e-05 ***
## Residuals 128 0.06504 0.0005081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(BASIN), y=abs.res.HW)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ BASIN, data=form3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01931 0.003861 9.138 1.87e-07 ***
## Residuals 128 0.05409 0.000423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ BASIN, data=form3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 1.240e-32 2.479e-33 0.511 0.768
## Residuals 128 6.214e-31 4.854e-33ggplot(form3, aes(x=factor(BASIN), y=abs.res.OL)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ WATERSHED, data=form3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 3.847 0.3497 1.963 0.0378 *
## Residuals 122 21.736 0.1782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.D)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ WATERSHED, data=form3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 1.302 0.1184 1.005 0.446
## Residuals 122 14.368 0.1178A2.P1.R <- aov(abs.res.P1.R ~ WATERSHED, data=form3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.34 0.03093 0.226 0.995
## Residuals 122 16.71 0.13698ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.P1.R)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ WATERSHED, data=form3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 8.16 0.7422 2.267 0.015 *
## Residuals 122 39.94 0.3274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.LLSC)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ WATERSHED, data=form3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 5.086 0.4623 4.741 4.93e-06 ***
## Residuals 122 11.897 0.0975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.SBLL)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ WATERSHED, data=form3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.00851 0.0007737 0.944 0.501
## Residuals 122 0.09999 0.0008196ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.BD)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ WATERSHED, data=form3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01833 0.0016662 2.77 0.00304 **
## Residuals 122 0.07338 0.0006015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.CPD)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ WATERSHED, data=form3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03237 0.0029429 4.301 2.05e-05 ***
## Residuals 122 0.08347 0.0006842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ WATERSHED, data=form3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.0291 0.002646 0.75 0.688
## Residuals 122 0.4303 0.003527A2.DbL <- aov(abs.res.DbL ~ WATERSHED, data=form3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03993 0.003630 4.518 1.01e-05 ***
## Residuals 122 0.09801 0.000803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.DbL)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ WATERSHED, data=form3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01169 0.0010627 1.243 0.267
## Residuals 122 0.10435 0.0008553ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HL)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ WATERSHED, data=form3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01036 0.0009418 0.826 0.614
## Residuals 122 0.13905 0.0011397ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HD)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ WATERSHED, data=form3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01704 0.0015488 3.018 0.00136 **
## Residuals 122 0.06260 0.0005131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.HW)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ WATERSHED, data=form3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.02208 0.0020073 4.772 4.46e-06 ***
## Residuals 122 0.05131 0.0004206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ WATERSHED, data=form3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 2.610e-32 2.372e-33 0.476 0.915
## Residuals 122 6.077e-31 4.981e-33ggplot(form3, aes(x=factor(WATERSHED), y=abs.res.OL)) +
geom_boxplot()
Sailfin
Zones
library(ggplot2)
A.D <- aov(abs.res.D ~ QUARTILE, data=lat3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.306 0.7685 4.292 0.00635 **
## Residuals 130 23.277 0.1791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.D)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ QUARTILE, data=lat3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.159 0.05296 0.444 0.722
## Residuals 130 15.511 0.11932A.P1.R <- aov(abs.res.P1.R ~ QUARTILE, data=lat3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.065 0.02172 0.166 0.919
## Residuals 130 16.987 0.13067ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.P1.R)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ QUARTILE, data=lat3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.31 0.4354 1.209 0.309
## Residuals 130 46.80 0.3600A.SBLL <- aov(abs.res.SBLL ~ QUARTILE, data=lat3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.436 0.1454 1.142 0.335
## Residuals 130 16.546 0.1273ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.SBLL)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ QUARTILE, data=lat3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00259 0.0008622 1.058 0.369
## Residuals 130 0.10591 0.0008147ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.BD)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ QUARTILE, data=lat3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01249 0.004163 6.832 0.00026 ***
## Residuals 130 0.07922 0.000609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.CPD)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ QUARTILE, data=lat3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.02074 0.006912 9.448 1.08e-05 ***
## Residuals 130 0.09511 0.000732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.CPL)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ QUARTILE, data=lat3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.0082 0.002742 0.79 0.502
## Residuals 130 0.4511 0.003470ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.PreDL)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ QUARTILE, data=lat3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.0195 0.006501 7.136 0.000179 ***
## Residuals 130 0.1184 0.000911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.DbL)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ QUARTILE, data=lat3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00344 0.0011466 1.324 0.269
## Residuals 130 0.11260 0.0008661ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.HL)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ QUARTILE, data=lat3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00672 0.002239 2.04 0.111
## Residuals 130 0.14269 0.001098A.HW <- aov(abs.res.HW ~ QUARTILE, data=lat3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.01136 0.003788 7.212 0.000163 ***
## Residuals 130 0.06827 0.000525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.HW)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ QUARTILE, data=lat3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.00766 0.0025545 5.052 0.00242 **
## Residuals 130 0.06573 0.0005056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ QUARTILE, data=lat3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 2.17e-32 7.242e-33 1.538 0.208
## Residuals 130 6.12e-31 4.708e-33ggplot(lat3, aes(x=factor(QUARTILE), y=abs.res.OL)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ BASIN, data=lat3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 2.789 0.5579 3.133 0.0106 *
## Residuals 128 22.793 0.1781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.D)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ BASIN, data=lat3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.945 0.1891 1.643 0.153
## Residuals 128 14.725 0.1150A1.P1.R <- aov(abs.res.P1.R ~ BASIN, data=lat3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.159 0.03187 0.241 0.943
## Residuals 128 16.893 0.13197ggplot(lat3, aes(x=factor(BASIN), y=abs.res.P1.R)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ BASIN, data=lat3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 4.54 0.9080 2.668 0.025 *
## Residuals 128 43.57 0.3404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.LLSC)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ BASIN, data=lat3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.755 0.1510 1.191 0.317
## Residuals 128 16.228 0.1268ggplot(lat3, aes(x=factor(BASIN), y=abs.res.SBLL)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ BASIN, data=lat3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00316 0.0006313 0.767 0.575
## Residuals 128 0.10534 0.0008230ggplot(lat3, aes(x=factor(BASIN), y=abs.res.BD)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ BASIN, data=lat3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01713 0.003425 5.879 6.34e-05 ***
## Residuals 128 0.07458 0.000583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.CPD)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ BASIN, data=lat3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.02013 0.004025 5.383 0.000159 ***
## Residuals 128 0.09572 0.000748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ BASIN, data=lat3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.0222 0.004437 1.299 0.268
## Residuals 128 0.4372 0.003415ggplot(lat3, aes(x=factor(BASIN), y=abs.res.PreDL)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ BASIN, data=lat3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.03427 0.006855 8.464 6.05e-07 ***
## Residuals 128 0.10367 0.000810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.DbL)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ BASIN, data=lat3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00766 0.0015321 1.809 0.115
## Residuals 128 0.10838 0.0008467ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HL)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ BASIN, data=lat3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.00684 0.001367 1.228 0.3
## Residuals 128 0.14257 0.001114ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HD)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ BASIN, data=lat3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01460 0.0029194 5.746 8.11e-05 ***
## Residuals 128 0.06504 0.0005081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(BASIN), y=abs.res.HW)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ BASIN, data=lat3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 0.01931 0.003861 9.138 1.87e-07 ***
## Residuals 128 0.05409 0.000423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ BASIN, data=lat3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 1.240e-32 2.479e-33 0.511 0.768
## Residuals 128 6.214e-31 4.854e-33ggplot(lat3, aes(x=factor(BASIN), y=abs.res.OL)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ WATERSHED, data=lat3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 3.847 0.3497 1.963 0.0378 *
## Residuals 122 21.736 0.1782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.D)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ WATERSHED, data=lat3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 1.302 0.1184 1.005 0.446
## Residuals 122 14.368 0.1178A2.P1.R <- aov(abs.res.P1.R ~ WATERSHED, data=lat3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.34 0.03093 0.226 0.995
## Residuals 122 16.71 0.13698ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.P1.R)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ WATERSHED, data=lat3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 8.16 0.7422 2.267 0.015 *
## Residuals 122 39.94 0.3274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.LLSC)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ WATERSHED, data=lat3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 5.086 0.4623 4.741 4.93e-06 ***
## Residuals 122 11.897 0.0975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.SBLL)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ WATERSHED, data=lat3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.00851 0.0007737 0.944 0.501
## Residuals 122 0.09999 0.0008196ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.BD)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ WATERSHED, data=lat3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01833 0.0016662 2.77 0.00304 **
## Residuals 122 0.07338 0.0006015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.CPD)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ WATERSHED, data=lat3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03237 0.0029429 4.301 2.05e-05 ***
## Residuals 122 0.08347 0.0006842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ WATERSHED, data=lat3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.0291 0.002646 0.75 0.688
## Residuals 122 0.4303 0.003527A2.DbL <- aov(abs.res.DbL ~ WATERSHED, data=lat3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.03993 0.003630 4.518 1.01e-05 ***
## Residuals 122 0.09801 0.000803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.DbL)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ WATERSHED, data=lat3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01169 0.0010627 1.243 0.267
## Residuals 122 0.10435 0.0008553ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HL)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ WATERSHED, data=lat3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01036 0.0009418 0.826 0.614
## Residuals 122 0.13905 0.0011397ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HD)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ WATERSHED, data=lat3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.01704 0.0015488 3.018 0.00136 **
## Residuals 122 0.06260 0.0005131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.HW)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ WATERSHED, data=lat3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 0.02208 0.0020073 4.772 4.46e-06 ***
## Residuals 122 0.05131 0.0004206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ WATERSHED, data=lat3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 2.610e-32 2.372e-33 0.476 0.915
## Residuals 122 6.077e-31 4.981e-33ggplot(lat3, aes(x=factor(WATERSHED), y=abs.res.OL)) +
geom_boxplot()
Both
Zones
library(ggplot2)
A.D <- aov(abs.res.D ~ SPP*QUARTILE, data=raw3)
summary(A.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.408 0.52344
## QUARTILE 3 0.45 0.1510 0.970 0.40715
## SPP:QUARTILE 3 3.22 1.0719 6.888 0.00017 ***
## Residuals 292 45.44 0.1556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A.P1 <- aov(abs.res.P1 ~ SPP*QUARTILE, data=raw3)
summary(A.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.554 0.214
## QUARTILE 3 1.10 0.3663 1.977 0.117
## SPP:QUARTILE 3 0.73 0.2427 1.310 0.271
## Residuals 292 54.10 0.1853A.P1.R <- aov(abs.res.P1.R ~ SPP*QUARTILE, data=raw3)
summary(A.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.423 0.000824 ***
## QUARTILE 3 0.13 0.0434 0.276 0.842918
## SPP:QUARTILE 3 0.06 0.0189 0.120 0.948162
## Residuals 292 45.97 0.1574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A.LLSC <- aov(abs.res.LLSC ~ SPP*QUARTILE, data=raw3)
summary(A.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 2.940 0.0875 .
## QUARTILE 3 1.35 0.4506 0.945 0.4190
## SPP:QUARTILE 3 3.42 1.1399 2.391 0.0688 .
## Residuals 292 139.19 0.4767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.SBLL <- aov(abs.res.SBLL ~ SPP*QUARTILE, data=raw3)
summary(A.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 4.859 0.0283 *
## QUARTILE 3 0.78 0.2591 1.930 0.1248
## SPP:QUARTILE 3 0.12 0.0390 0.291 0.8320
## Residuals 292 39.20 0.1342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A.BD <- aov(abs.res.BD ~ SPP*QUARTILE, data=raw3)
summary(A.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0023 0.002282 0.608 0.4363
## QUARTILE 3 0.0161 0.005364 1.429 0.2345
## SPP:QUARTILE 3 0.0289 0.009643 2.568 0.0546 .
## Residuals 292 1.0963 0.003755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A.CPD <- aov(abs.res.CPD ~ SPP*QUARTILE, data=raw3)
summary(A.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.900 0.02764 *
## QUARTILE 3 0.00786 0.0026201 4.108 0.00707 **
## SPP:QUARTILE 3 0.00713 0.0023775 3.728 0.01176 *
## Residuals 292 0.18622 0.0006377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A.CPL <- aov(abs.res.CPL ~ SPP*QUARTILE, data=raw3)
summary(A.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.986836
## QUARTILE 3 0.01640 0.005468 6.588 0.000254 ***
## SPP:QUARTILE 3 0.02033 0.006777 8.166 3.08e-05 ***
## Residuals 292 0.24236 0.000830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.CPL, fill=SPP)) +
geom_boxplot()
A.PreDL <- aov(abs.res.PreDL ~ SPP*QUARTILE, data=raw3)
summary(A.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0012 0.001166 0.501 0.480
## QUARTILE 3 0.0053 0.001774 0.763 0.516
## SPP:QUARTILE 3 0.0055 0.001820 0.783 0.504
## Residuals 292 0.6792 0.002326ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.PreDL, fill=SPP)) +
geom_boxplot()
A.DbL <- aov(abs.res.DbL ~ SPP*QUARTILE, data=raw3)
summary(A.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00556 0.005557 5.888 0.01585 *
## QUARTILE 3 0.01021 0.003404 3.607 0.01382 *
## SPP:QUARTILE 3 0.01319 0.004397 4.659 0.00338 **
## Residuals 292 0.27557 0.000944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A.HL <- aov(abs.res.HL ~ SPP*QUARTILE, data=raw3)
summary(A.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.861 0.3541
## QUARTILE 3 0.02099 0.006995 7.421 8.33e-05 ***
## SPP:QUARTILE 3 0.01029 0.003432 3.640 0.0132 *
## Residuals 292 0.27527 0.000943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A.HD <- aov(abs.res.HD ~ SPP*QUARTILE, data=raw3)
summary(A.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## QUARTILE 3 0.00443 0.0014757 1.522 0.209
## SPP:QUARTILE 3 0.00582 0.0019390 1.999 0.114
## Residuals 292 0.28319 0.0009698A.HW <- aov(abs.res.HW ~ SPP*QUARTILE, data=raw3)
summary(A.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.861 0.09184 .
## QUARTILE 3 0.00917 0.0030561 4.103 0.00712 **
## SPP:QUARTILE 3 0.00710 0.0023664 3.177 0.02448 *
## Residuals 292 0.21750 0.0007449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A.SnL <- aov(abs.res.SnL ~ SPP*QUARTILE, data=raw3)
summary(A.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.000095 0.163 0.6867
## QUARTILE 3 0.01743 0.005809 9.995 2.73e-06 ***
## SPP:QUARTILE 3 0.00494 0.001647 2.833 0.0386 *
## Residuals 292 0.16972 0.000581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A.OL <- aov(abs.res.OL ~ SPP*QUARTILE, data=raw3)
summary(A.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.108 0.1476
## QUARTILE 3 1.830e-32 6.103e-33 2.617 0.0512 .
## SPP:QUARTILE 3 5.800e-33 1.948e-33 0.835 0.4754
## Residuals 292 6.809e-31 2.332e-33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(QUARTILE), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Basins
A1.D <- aov(abs.res.D ~ SPP*BASIN, data=raw3)
summary(A1.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.402 0.52666
## BASIN 6 3.12 0.5205 3.293 0.00377 **
## SPP:BASIN 3 0.31 0.1028 0.650 0.58344
## Residuals 289 45.68 0.1581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A1.P1 <- aov(abs.res.P1 ~ SPP*BASIN, data=raw3)
summary(A1.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.528 0.217
## BASIN 6 0.97 0.1613 0.856 0.528
## SPP:BASIN 3 0.53 0.1751 0.930 0.427
## Residuals 289 54.43 0.1883A1.P1.R <- aov(abs.res.P1.R ~ SPP*BASIN, data=raw3)
summary(A1.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.421 0.000826 ***
## BASIN 6 0.51 0.0845 0.537 0.780341
## SPP:BASIN 3 0.15 0.0485 0.308 0.819718
## Residuals 289 45.50 0.1575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A1.LLSC <- aov(abs.res.LLSC ~ SPP*BASIN, data=raw3)
summary(A1.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 2.999 0.0844 .
## BASIN 6 6.83 1.1376 2.435 0.0260 *
## SPP:BASIN 3 2.11 0.7026 1.504 0.2137
## Residuals 289 135.02 0.4672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.LLSC, fill=SPP)) +
geom_boxplot()
A1.SBLL <- aov(abs.res.SBLL ~ SPP*BASIN, data=raw3)
summary(A1.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 4.895 0.0277 *
## BASIN 6 1.07 0.1785 1.340 0.2393
## SPP:BASIN 3 0.51 0.1716 1.288 0.2788
## Residuals 289 38.51 0.1332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A1.BD <- aov(abs.res.BD ~ SPP*BASIN, data=raw3)
summary(A1.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0023 0.002282 0.590 0.443
## BASIN 6 0.0161 0.002675 0.691 0.657
## SPP:BASIN 3 0.0068 0.002270 0.586 0.624
## Residuals 289 1.1185 0.003870ggplot(raw3, aes(x=factor(BASIN), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A1.CPD <- aov(abs.res.CPD ~ SPP*BASIN, data=raw3)
summary(A1.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.984 0.026343 *
## BASIN 6 0.01576 0.0026273 4.191 0.000464 ***
## SPP:BASIN 3 0.00428 0.0014261 2.275 0.080070 .
## Residuals 289 0.18117 0.0006269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A1.CPL <- aov(abs.res.CPL ~ SPP*BASIN, data=raw3)
summary(A1.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.987
## BASIN 6 0.03228 0.005380 6.320 2.92e-06 ***
## SPP:BASIN 3 0.00078 0.000259 0.304 0.822
## Residuals 289 0.24603 0.000851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.PreDL <- aov(abs.res.PreDL ~ SPP*BASIN, data=raw3)
summary(A1.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0012 0.001166 0.507 0.477
## BASIN 6 0.0132 0.002195 0.954 0.456
## SPP:BASIN 3 0.0122 0.004078 1.773 0.152
## Residuals 289 0.6646 0.002300ggplot(raw3, aes(x=factor(BASIN), y=abs.res.PreDL, fill=SPP)) +
geom_boxplot()
A1.DbL <- aov(abs.res.DbL ~ SPP*BASIN, data=raw3)
summary(A1.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00556 0.005557 6.191 0.013405 *
## BASIN 6 0.02192 0.003653 4.070 0.000618 ***
## SPP:BASIN 3 0.01766 0.005886 6.557 0.000265 ***
## Residuals 289 0.25940 0.000898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A1.HL <- aov(abs.res.HL ~ SPP*BASIN, data=raw3)
summary(A1.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.827 0.36379
## BASIN 6 0.02039 0.003398 3.463 0.00255 **
## SPP:BASIN 3 0.00256 0.000852 0.868 0.45782
## Residuals 289 0.28360 0.000981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A1.HD <- aov(abs.res.HD ~ SPP*BASIN, data=raw3)
summary(A1.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## BASIN 6 0.00478 0.0007959 0.808 0.565
## SPP:BASIN 3 0.00382 0.0012743 1.293 0.277
## Residuals 289 0.28484 0.0009856ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HD, fill=SPP)) +
geom_boxplot()
A1.HW <- aov(abs.res.HW ~ SPP*BASIN, data=raw3)
summary(A1.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.825 0.09387 .
## BASIN 6 0.01505 0.0025085 3.326 0.00349 **
## SPP:BASIN 3 0.00076 0.0002544 0.337 0.79838
## Residuals 289 0.21795 0.0007542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(BASIN), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A1.SnL <- aov(abs.res.SnL ~ SPP*BASIN, data=raw3)
summary(A1.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.000095 0.162 0.688
## BASIN 6 0.02054 0.003424 5.849 8.99e-06 ***
## SPP:BASIN 3 0.00238 0.000794 1.357 0.256
## Residuals 289 0.16916 0.000585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A1.OL <- aov(abs.res.OL ~ SPP*BASIN, data=raw3)
summary(A1.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.054 0.153
## BASIN 6 9.200e-33 1.535e-33 0.641 0.697
## SPP:BASIN 3 4.100e-33 1.358e-33 0.567 0.637
## Residuals 289 6.917e-31 2.394e-33ggplot(raw3, aes(x=factor(BASIN), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Watersheds
A2.D <- aov(abs.res.D ~ SPP*WATERSHED, data=raw3)
summary(A2.D)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.06 0.0635 0.415 0.5198
## WATERSHED 13 4.33 0.3331 2.178 0.0106 *
## SPP:WATERSHED 5 1.96 0.3922 2.564 0.0274 *
## Residuals 280 42.82 0.1529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.D, fill=SPP)) +
geom_boxplot()
A2.P1 <- aov(abs.res.P1 ~ SPP*WATERSHED, data=raw3)
summary(A2.P1)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.29 0.2878 1.568 0.2116
## WATERSHED 13 3.73 0.2868 1.562 0.0957 .
## SPP:WATERSHED 5 0.78 0.1569 0.855 0.5120
## Residuals 280 51.41 0.1836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.P1.R <- aov(abs.res.P1.R ~ SPP*WATERSHED, data=raw3)
summary(A2.P1.R)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.80 1.7983 11.145 0.000957 ***
## WATERSHED 13 0.82 0.0629 0.390 0.972416
## SPP:WATERSHED 5 0.16 0.0322 0.199 0.962521
## Residuals 280 45.18 0.1614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.P1.R, fill=SPP)) +
geom_boxplot()
A2.LLSC <- aov(abs.res.LLSC ~ SPP*WATERSHED, data=raw3)
summary(A2.LLSC)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 1.40 1.4013 3.299 0.0704 .
## WATERSHED 13 20.28 1.5596 3.672 2.15e-05 ***
## SPP:WATERSHED 5 4.75 0.9502 2.237 0.0509 .
## Residuals 280 118.93 0.4248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.LLSC, fill=SPP)) +
geom_boxplot()
A2.SBLL <- aov(abs.res.SBLL ~ SPP*WATERSHED, data=raw3)
summary(A2.SBLL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.65 0.6523 5.611 0.0185 *
## WATERSHED 13 2.98 0.2289 1.969 0.0233 *
## SPP:WATERSHED 5 4.57 0.9138 7.861 6.06e-07 ***
## Residuals 280 32.55 0.1162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.SBLL, fill=SPP)) +
geom_boxplot()
A2.BD <- aov(abs.res.BD ~ SPP*WATERSHED, data=raw3)
summary(A2.BD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0023 0.002282 0.590 0.443
## WATERSHED 13 0.0310 0.002386 0.617 0.840
## SPP:WATERSHED 5 0.0275 0.005491 1.420 0.217
## Residuals 280 1.0829 0.003867ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.BD, fill=SPP)) +
geom_boxplot()
A2.CPD <- aov(abs.res.CPD ~ SPP*WATERSHED, data=raw3)
summary(A2.CPD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00312 0.0031247 4.943 0.0270 *
## WATERSHED 13 0.01690 0.0012999 2.056 0.0169 *
## SPP:WATERSHED 5 0.00731 0.0014624 2.313 0.0441 *
## Residuals 280 0.17700 0.0006322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.CPD, fill=SPP)) +
geom_boxplot()
A2.CPL <- aov(abs.res.CPL ~ SPP*WATERSHED, data=raw3)
summary(A2.CPL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.000000 0.000 0.9866
## WATERSHED 13 0.04566 0.003512 4.406 8.64e-07 ***
## SPP:WATERSHED 5 0.01021 0.002043 2.563 0.0275 *
## Residuals 280 0.22322 0.000797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.PreDL <- aov(abs.res.PreDL ~ SPP*WATERSHED, data=raw3)
summary(A2.PreDL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.0012 0.001166 0.498 0.481
## WATERSHED 13 0.0220 0.001688 0.722 0.742
## SPP:WATERSHED 5 0.0128 0.002569 1.098 0.362
## Residuals 280 0.6552 0.002340A2.DbL <- aov(abs.res.DbL ~ SPP*WATERSHED, data=raw3)
summary(A2.DbL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00556 0.005557 6.321 0.0125 *
## WATERSHED 13 0.03949 0.003038 3.456 5.48e-05 ***
## SPP:WATERSHED 5 0.01333 0.002666 3.033 0.0110 *
## Residuals 280 0.24615 0.000879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.DbL, fill=SPP)) +
geom_boxplot()
A2.HL <- aov(abs.res.HL ~ SPP*WATERSHED, data=raw3)
summary(A2.HL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00081 0.000812 0.896 0.345
## WATERSHED 13 0.05036 0.003874 4.275 1.54e-06 ***
## SPP:WATERSHED 5 0.00247 0.000494 0.546 0.742
## Residuals 280 0.25372 0.000906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HL, fill=SPP)) +
geom_boxplot()
A2.HD <- aov(abs.res.HD ~ SPP*WATERSHED, data=raw3)
summary(A2.HD)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00000 0.0000000 0.000 0.999
## WATERSHED 13 0.01725 0.0013271 1.371 0.173
## SPP:WATERSHED 5 0.00519 0.0010375 1.072 0.376
## Residuals 280 0.27100 0.0009678ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HD, fill=SPP)) +
geom_boxplot()
A2.HW <- aov(abs.res.HW ~ SPP*WATERSHED, data=raw3)
summary(A2.HW)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00213 0.0021307 2.862 0.0918 .
## WATERSHED 13 0.02008 0.0015446 2.075 0.0157 *
## SPP:WATERSHED 5 0.00521 0.0010418 1.399 0.2246
## Residuals 280 0.20848 0.0007446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.HW, fill=SPP)) +
geom_boxplot()
A2.SnL <- aov(abs.res.SnL ~ SPP*WATERSHED, data=raw3)
summary(A2.SnL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 0.00009 0.0000948 0.180 0.672
## WATERSHED 13 0.04027 0.0030976 5.890 1.23e-09 ***
## SPP:WATERSHED 5 0.00457 0.0009144 1.739 0.126
## Residuals 280 0.14725 0.0005259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A2.OL <- aov(abs.res.OL ~ SPP*WATERSHED, data=raw3)
summary(A2.OL)## Df Sum Sq Mean Sq F value Pr(>F)
## SPP 1 4.900e-33 4.915e-33 2.037 0.155
## WATERSHED 13 2.900e-32 2.231e-33 0.925 0.528
## SPP:WATERSHED 5 2.000e-34 4.600e-35 0.019 1.000
## Residuals 280 6.758e-31 2.414e-33ggplot(raw3, aes(x=factor(WATERSHED), y=abs.res.OL, fill=SPP)) +
geom_boxplot()
Standardized
DON’T KNOW IF THIS IS USEFUL
The ANOVAs above focus on differences of particular traits as a factor of species and location. If we want to get an idea of variation in general as a factor of species and location, we can standardize the residuals (essentially unitless z-scores of residuals).
sd.res.D <- append(abs(sd.lat.D), abs(sd.form.D))
sd.res.P1 <- append(abs(sd.lat.P1), abs(sd.form.P1))
sd.res.P1.R <- append(abs(sd.lat.P1.R), abs(sd.form.P1.R))
sd.res.LLSC<- append(abs(sd.lat.LLSC), abs(sd.form.LLSC))
sd.res.SBLL<- append(abs(sd.lat.SBLL), abs(sd.form.SBLL))
sd.res.BD<- append(abs(sd.lat.BD), abs(sd.form.BD))
sd.res.CPD<- append(abs(sd.lat.CPD), abs(sd.form.CPD))
sd.res.CPL<- append(abs(sd.lat.CPL), abs(sd.form.CPL))
sd.res.PreDL <- append(abs(sd.lat.PreDL), abs(sd.form.PreDL))
sd.res.DbL <- append(abs(sd.lat.DbL), abs(sd.form.DbL))
sd.res.HL<- append(abs(sd.lat.HL), abs(sd.form.HL))
sd.res.HD<- append(abs(sd.lat.HD), abs(sd.form.HD))
sd.res.HW <- append(abs(sd.lat.HW), abs(sd.form.HW))
sd.res.SnL <- append(abs(sd.lat.SnL), abs(sd.form.SnL))
sd.res.OL <- append(abs(sd.lat.OL), abs(sd.form.OL))
raw4 <- cbind(raw3, sd.res.D, sd.res.P1, sd.res.P1.R, sd.res.LLSC, sd.res.SBLL, sd.res.BD, sd.res.CPD, sd.res.CPL, sd.res.PreDL, sd.res.DbL, sd.res.HL, sd.res.HD, sd.res.HW, sd.res.SnL, sd.res.OL)
raw5 <- cbind(raw4[1:14], stack(raw4[53:68])) ## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discardedlat.raw5 <- raw5[raw5$SPP == "p.latipinna",]
form.raw5 <- raw5[raw5$SPP == "p.formosa",]
######ZONES#####
A3.lat <- aov(values~QUARTILE, data=lat.raw5)
summary(A3.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.3 0.1037 0.369 0.775
## Residuals 2140 601.4 0.2810A3.form <- aov(values~QUARTILE, data=form.raw5)
summary(A3.form)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 1.4 0.4703 1.579 0.192
## Residuals 2652 789.9 0.2979#between species
A3 <- aov(values~QUARTILE*SPP, data=raw5)
summary(A3)## Df Sum Sq Mean Sq F value Pr(>F)
## QUARTILE 3 0.6 0.2047 0.705 0.5489
## SPP 1 1.0 1.0000 3.444 0.0635 .
## QUARTILE:SPP 3 1.1 0.3643 1.255 0.2882
## Residuals 4792 1391.3 0.2903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw5, aes(x=factor(QUARTILE), y=values, fill=SPP)) +
geom_boxplot()
######BASINS#####
A4.lat <- aov(values~BASIN, data=lat.raw5)
summary(A4.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 5 1.1 0.2133 0.759 0.579
## Residuals 2138 600.6 0.2809A4.form <- aov(values~BASIN, data=form.raw5)
summary(A4.form)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 4 0.9 0.2307 0.774 0.542
## Residuals 2651 790.4 0.2982#between species
A4 <- aov(values~BASIN*SPP, data=raw5)
summary(A4)## Df Sum Sq Mean Sq F value Pr(>F)
## BASIN 6 2.0 0.3251 1.119 0.3482
## SPP 1 0.8 0.7979 2.747 0.0975 .
## BASIN:SPP 3 0.2 0.0752 0.259 0.8551
## Residuals 4789 1391.0 0.2905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw5, aes(x=factor(BASIN), y=values, fill=SPP)) +
geom_boxplot()
#####WATERSHEDS#####
A5.lat <- aov(values~WATERSHED, data=lat.raw5)
summary(A5.lat)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 11 6.1 0.5514 1.974 0.0273 *
## Residuals 2132 595.6 0.2794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1A5.form <- aov(values~WATERSHED, data=form.raw5)
summary(A5.form)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 7 5.1 0.7267 2.447 0.0168 *
## Residuals 2648 786.2 0.2969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#between species
A5 <- aov(values~WATERSHED*SPP, data=raw5)
summary(A5)## Df Sum Sq Mean Sq F value Pr(>F)
## WATERSHED 13 6.5 0.4965 1.717 0.05087 .
## SPP 1 0.9 0.9387 3.247 0.07161 .
## WATERSHED:SPP 5 4.7 0.9487 3.282 0.00583 **
## Residuals 4780 1381.8 0.2891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(raw5, aes(x=factor(WATERSHED), y=values, fill=SPP)) +
geom_boxplot()
PCA analysis
PCA
LOGAN: CHECK THAT EACH VARIABLES IS NEAR NORMALLY DISTRIBUTED. IF NOT, LOG TRANSFORM BEFORE PCA. ALSO CHECK THAT PCA CALCULATES Z SCORES AND PLOTS BASED ON THAT; IF NOT CONVERT TO Z SCORES THEN RUN PCA.
In this analysis, I will compare the principle components after centering and scaling the data. A PCA analysis will help us determine what aspects of morphology influence the variation in our data the most without worrying about differences in scales/measurements. Currently, data consists of 116 Sailfin and 186 Amazon.
Chart
(chart <- matrix(c("Variable chart:", "D: dorsal ray count", "P1: left pectoral ray count", "P2.R: right pelvic rays", "P2.L: left pelvic rays", "A: anal rays", "P1.R: right pectoral ray count", "LLSC: lateral line scale count", "SALL: scales above lateral line", "SBLL: scales below lateral line", "SBDF: scales before dorsal fin", "TL: total length", "SL: standard length", "BD: body depth", "CPD: caudal peduncle depth", "CPL: caudal peduncle length", "PreDL: pre-dorsal length", "DbL: dorsal base length", "HL/HW/HD: head length/width/depth", "SnL: snout length", "OL: ocular length"), ncol=1, byrow=TRUE))## [,1]
## [1,] "Variable chart:"
## [2,] "D: dorsal ray count"
## [3,] "P1: left pectoral ray count"
## [4,] "P2.R: right pelvic rays"
## [5,] "P2.L: left pelvic rays"
## [6,] "A: anal rays"
## [7,] "P1.R: right pectoral ray count"
## [8,] "LLSC: lateral line scale count"
## [9,] "SALL: scales above lateral line"
## [10,] "SBLL: scales below lateral line"
## [11,] "SBDF: scales before dorsal fin"
## [12,] "TL: total length"
## [13,] "SL: standard length"
## [14,] "BD: body depth"
## [15,] "CPD: caudal peduncle depth"
## [16,] "CPL: caudal peduncle length"
## [17,] "PreDL: pre-dorsal length"
## [18,] "DbL: dorsal base length"
## [19,] "HL/HW/HD: head length/width/depth"
## [20,] "SnL: snout length"
## [21,] "OL: ocular length"Loadings
raw1a <- subset(raw1, select = -c(16:18) )
PCA <- prcomp(raw1a[, 15:32], center=TRUE, scale. = TRUE) #includes all but pelvic traits, since they are the same in all species
summary(PCA)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.961 1.6259 1.08642 1.03324 0.97942 0.93729 0.80773
## Proportion of Variance 0.487 0.1469 0.06557 0.05931 0.05329 0.04881 0.03625
## Cumulative Proportion 0.487 0.6339 0.69942 0.75873 0.81203 0.86083 0.89708
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.77182 0.54803 0.48623 0.46998 0.3746 0.31618 0.27529
## Proportion of Variance 0.03309 0.01669 0.01313 0.01227 0.0078 0.00555 0.00421
## Cumulative Proportion 0.93017 0.94686 0.95999 0.97226 0.9801 0.98561 0.98982
## PC15 PC16 PC17 PC18
## Standard deviation 0.24862 0.23425 0.22644 0.12339
## Proportion of Variance 0.00343 0.00305 0.00285 0.00085
## Cumulative Proportion 0.99326 0.99631 0.99915 1.00000(loadings <- PCA$rotation[, 1:5])## PC1 PC2 PC3 PC4 PC5
## D -0.025108263 -0.55543398 -0.204065448 -0.0432146833 0.01322127
## A -0.007657967 0.05864241 -0.516347575 -0.3997522212 -0.61191359
## P1.R -0.017685363 0.42264377 -0.121615925 -0.1355579521 -0.19302219
## LLSC -0.078654354 -0.00632404 -0.479204155 0.5271970363 0.29628190
## SALL -0.036439305 -0.05721360 0.519500405 0.4340355978 -0.54808415
## SBLL -0.048429591 -0.05003098 -0.357637604 0.5491399767 -0.37340026
## SBDF -0.004651513 0.52548030 -0.087495119 0.1774820105 0.14584307
## SL -0.329799007 0.08491057 0.005044672 -0.0293415223 0.02269537
## BD -0.320842190 -0.08571029 -0.005338963 0.0176425136 0.02068217
## CPD -0.326190468 -0.02557070 0.047138928 -0.0583694362 -0.01044821
## CPL -0.315318535 0.11009355 0.006579877 -0.0094553119 0.04336306
## PreDL -0.297122285 0.23349788 0.050939933 -0.0182778666 -0.02232858
## DbL -0.245253913 -0.37105730 -0.047255019 -0.0675018348 0.01623612
## HL -0.289172502 0.02061032 -0.069719784 -0.0865238724 0.04786649
## HD -0.321400173 -0.04634301 0.022508677 -0.0496468119 0.06233383
## HW -0.321926126 0.04469920 0.092090639 0.0326037283 -0.03172203
## SnL -0.214460008 -0.05014512 0.130882266 0.0017726331 -0.16379453
## OL -0.294285922 0.02932691 -0.061230019 -0.0001186146 0.03931992sorted.loadings.1 <- loadings[order(loadings[, 1]), 1]
myTitle <- "Loadings Plot for PC1"
myXlab <- "Variable Loadings"
dotchart(sorted.loadings.1, main=myTitle, xlab=myXlab, cex=1.5, col="red")
sorted.loadings.2 <- loadings[order(loadings[, 2]), 2]
myTitle <- "Loadings Plot for PC2"
myXlab <- "Variable Loadings"
dotchart(sorted.loadings.2, main=myTitle, xlab=myXlab, cex=1.5, col="red")
sorted.loadings.3 <- loadings[order(loadings[, 3]), 3]
myTitle <- "Loadings Plot for PC3"
myXlab <- "Variable Loadings"
dotchart(sorted.loadings.3, main=myTitle, xlab=myXlab, cex=1.5, col="red")
VM_PCA <- varimax(PCA$rotation)
VM_PCA## $loadings
##
## Loadings:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16
## D -1
## A -1
## P1.R -1
## LLSC 1
## SALL 1
## SBLL -1
## SBDF 1
## SL
## BD -1
## CPD
## CPL -1
## PreDL -1
## DbL 1
## HL 1
## HD 1
## HW -1
## SnL -1
## OL -1
## PC17 PC18
## D
## A
## P1.R
## LLSC
## SALL
## SBLL
## SBDF
## SL 1
## BD
## CPD 1
## CPL
## PreDL
## DbL
## HL
## HD
## HW
## SnL
## OL
##
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056
## Cumulative Var 0.056 0.111 0.167 0.222 0.278 0.333 0.389 0.444 0.500 0.556
## PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056
## Cumulative Var 0.611 0.667 0.722 0.778 0.833 0.889 0.944 1.000
##
## $rotmat
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.3219261237 0.02510828 -0.036439306 -0.078654368 0.007657989
## [2,] -0.0446992069 0.55543394 -0.057213641 -0.006324052 -0.058642377
## [3,] -0.0920906421 0.20406545 0.519500670 -0.479204308 0.516347268
## [4,] -0.0326037378 0.04321468 0.434035867 0.527197159 0.399751969
## [5,] 0.0317220321 -0.01322128 -0.548083772 0.296281819 0.611913921
## [6,] 0.0136429476 -0.04394442 0.388233014 0.529602504 -0.351562759
## [7,] 0.0300222480 0.15540493 -0.155600536 -0.172681388 -0.193112792
## [8,] -0.0263614684 0.04528755 0.164534883 -0.167951397 -0.124539401
## [9,] 0.1342223346 -0.08204914 0.087627176 0.012441859 0.034150254
## [10,] -0.1036782848 -0.44030311 0.074088336 -0.172666056 0.006036049
## [11,] -0.2364987676 0.26371383 -0.126067295 0.092162379 -0.115021117
## [12,] -0.0465619994 0.53501853 -0.002276466 0.093586039 -0.048447786
## [13,] 0.4763014542 0.21639007 0.053353969 -0.039597216 -0.018100105
## [14,] 0.0004626634 -0.04444238 0.026277094 0.010977539 -0.016082025
## [15,] -0.0928579707 -0.05365086 0.003045335 -0.098490444 -0.034010892
## [16,] -0.5107289452 -0.05121511 -0.025888687 0.027609857 -0.024839336
## [17,] 0.5343922581 -0.09062037 0.033283349 0.023118943 0.003021833
## [18,] 0.1151678784 0.01195750 0.018346326 0.002323942 0.009991313
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.048429569 0.01768536 0.214460009 -0.28917250 -0.0046515118
## [2,] 0.050030984 -0.42264377 0.050145118 0.02061032 0.5254803108
## [3,] 0.357637452 0.12161593 -0.130882266 -0.06971978 -0.0874951163
## [4,] -0.549139829 0.13555795 -0.001772633 -0.08652387 0.1774820114
## [5,] 0.373400344 0.19302219 0.163794533 0.04786649 0.1458430706
## [6,] 0.635932024 0.15025751 -0.038190321 -0.02887285 0.0738524609
## [7,] -0.110709157 0.74061288 -0.376784024 0.02128196 0.3299828233
## [8,] -0.051549852 0.31444972 0.866326081 0.15705131 0.1017346315
## [9,] -0.005265660 -0.01312401 -0.088985564 0.80807722 0.1093110673
## [10,] 0.060467289 -0.13242476 -0.002448353 -0.22909667 0.6987442332
## [11,] -0.035905583 0.20660581 0.055806882 -0.30184685 -0.1331149106
## [12,] 0.017701395 -0.08227240 -0.007200975 0.05666880 -0.0053772537
## [13,] -0.016079039 0.08390788 -0.021282779 0.00476731 0.1277171583
## [14,] -0.023990107 -0.02159300 -0.007990821 -0.22466796 0.0182482439
## [15,] -0.020446257 0.03077480 -0.017531406 0.02720849 0.0477060736
## [16,] 0.001789952 0.07105234 0.040017163 0.07723549 0.0329101541
## [17,] -0.019811963 0.00238789 0.006509850 -0.13359655 -0.0008947563
## [18,] -0.002592149 -0.00092466 0.005121886 -0.02270106 -0.0280220293
## [,11] [,12] [,13] [,14] [,15]
## [1,] 0.2942859234 0.3153192664 -0.24525297 -0.32140019 0.2971222926
## [2,] -0.0293269102 -0.1100924591 -0.37105766 -0.04634301 -0.2334978927
## [3,] 0.0612300201 -0.0065797470 -0.04725504 0.02250868 -0.0509399352
## [4,] 0.0001186154 0.0094555097 -0.06750181 -0.04964681 0.0182778668
## [5,] -0.0393199164 -0.0433631099 0.01623598 0.06233384 0.0223285794
## [6,] -0.0324815176 -0.0190456592 -0.05686694 -0.02430586 0.0006229111
## [7,] -0.1366527484 0.1489676508 -0.14611544 0.03068266 -0.0646743205
## [8,] -0.1130938511 -0.0004479098 0.01634738 0.11054770 -0.0852014182
## [9,] 0.5008070786 0.0580593824 -0.01493268 0.11738705 0.0245567369
## [10,] 0.1981464875 0.1331502612 0.30269250 0.03103419 0.1110463216
## [11,] 0.7557661500 -0.1778142641 0.07766334 0.02973031 -0.1363150999
## [12,] -0.0696021143 0.3843471014 0.36505820 0.29552124 0.4728614204
## [13,] -0.0341062726 -0.5167317622 0.52407171 -0.30400581 0.1226784030
## [14,] 0.0109614331 -0.1210235434 0.10846697 0.70825624 -0.0355987528
## [15,] -0.0070553388 -0.5203739096 -0.35389964 0.16845443 0.6281445377
## [16,] -0.0242978744 -0.1076411066 -0.07390241 -0.24068768 0.3092391095
## [17,] 0.0772508602 -0.0390618306 -0.33669606 0.29203155 0.0225063642
## [18,] 0.0270806510 0.3128669202 -0.09290549 -0.07313309 0.2831870115
## [,16] [,17] [,18]
## [1,] 0.320842156 -0.326190480 -0.329799006
## [2,] 0.085710295 -0.025570694 0.084910572
## [3,] 0.005338974 0.047138935 0.005044672
## [4,] -0.017642508 -0.058369435 -0.029341522
## [5,] -0.020682173 -0.010448216 0.022695375
## [6,] -0.010450947 -0.054650898 -0.003282193
## [7,] 0.035665976 -0.046116425 -0.028552797
## [8,] -0.063816178 0.022817476 0.047558720
## [9,] 0.135533534 -0.069632230 -0.034558486
## [10,] -0.167139303 0.090000176 -0.013181473
## [11,] -0.169309096 0.099637999 0.118722245
## [12,] -0.155683617 0.152330270 -0.214231096
## [13,] 0.186265919 0.034812983 0.106212379
## [14,] 0.579174916 -0.266359247 0.096040493
## [15,] -0.311068254 -0.238586348 -0.015193539
## [16,] 0.561394466 0.483048937 -0.032924944
## [17,] -0.035577532 0.687773168 -0.072990532
## [18,] 0.030876634 -0.006467397 0.889397637Plots
Component extractions
library(AMR)
library(ggplot2)
library(ggfortify)
(PCA$sdev ^ 2)## [1] 8.76586176 2.64347463 1.18030344 1.06757490 0.95926986 0.87851453
## [7] 0.65242074 0.59570413 0.30033241 0.23641483 0.22088004 0.14031638
## [13] 0.09996690 0.07578261 0.06181091 0.05487148 0.05127637 0.01522408evplot <- function(ev)
{
# Broken stick model (MacArthur 1957)
n <- length(ev)
bsm <- data.frame(j=seq(1:n), p=0)
bsm$p[1] <- 1/n
for (i in 2:n) bsm$p[i] <- bsm$p[i-1] + (1/(n + 1 - i))
bsm$p <- 100*bsm$p/n
# Plot eigenvalues and % of variation for each axis
op <- par(mfrow=c(2,1))
barplot(ev, main="Eigenvalues", col="bisque", las=2)
abline(h=mean(ev), col="red")
legend("topright", "Average eigenvalue", lwd=1, col=2, bty="n")
barplot(t(cbind(100*ev/sum(ev), bsm$p[n:1])), beside=TRUE,
main="% variation", col=c("bisque",2), las=2)
legend("topright", c("% eigenvalue", "Broken stick model"),
pch=15, col=c("bisque",2), bty="n")
par(op)
}
ev <- PCA$sdev^2
evplot(ev) #according to Kaiser-Guttman criteron, we can use the first 4 PCs, even though the broken stick model shows only the first above the red bar plot... not 100% confident I know what this means, but pretty sure PC1 is body size
PCA 1v2
plot1<- autoplot(PCA, data = raw1, colour='SPP', loadings=FALSE, loadings.label=FALSE, frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot1
By zone
plot2<- autoplot(PCA, data = raw1, colour='QUARTILE', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot2
By Basin
plot3<- autoplot(PCA, data = raw1, colour='BASIN', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot3
By watershed
plot4<- autoplot(PCA, data = raw1, colour='WATERSHED', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot4## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
PCA 2v3
plot5<- autoplot(PCA, x=2, y=3, data = raw1, colour='SPP', loadings=FALSE, loadings.label=FALSE, frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot5
By zone
plot6<- autoplot(PCA, x=2, y=3, data = raw1, colour='QUARTILE', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot6
By basin
plot7<- autoplot(PCA, x=2, y=3, data = raw1, colour='BASIN', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot7
By watershed
plot8<- autoplot(PCA, x=2, y=3, data = raw1, colour='WATERSHED', shape="SPP", frame=TRUE, frame.type='norm')+ ggtitle("PCA Plot of Morphology traits") + theme_minimal()
plot8## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
Filtered data
Now I will repeat analysis on a dataset that filters for fish above 32.5mm (this is the average for the range I found in literature, 28-37mm). This would confirm that all fish are likely adults, and remove any bias from juvenile proportions (ex. if juvenile head is proportionately larger than expected for body).
Initial steps
Data collection
library(dplyr)
rawF <- filter(raw1,SL>=32.5)Checking normality
Checking normality
I will use Shapiro-wilke, histograms, and QQ plots to determine what traits are normal. These will only be performed on continuous variables, as discrete variables are not normal by nature.
Conclusions: literally all of them are NOT normal… will log transform them and run parametric tests (t-test, F-test, ANOVA). Not sure what to do with the discrete variables…[Logan said to use non-parametric OR a glmer with poisson distribution]
SW Test
shapiro.test(rawF$SL)##
## Shapiro-Wilk normality test
##
## data: rawF$SL
## W = 0.88785, p-value = 9.323e-12shapiro.test(rawF$BD)##
## Shapiro-Wilk normality test
##
## data: rawF$BD
## W = 0.94379, p-value = 1.537e-07shapiro.test(rawF$CPD)##
## Shapiro-Wilk normality test
##
## data: rawF$CPD
## W = 0.9366, p-value = 3.381e-08shapiro.test(rawF$CPL)##
## Shapiro-Wilk normality test
##
## data: rawF$CPL
## W = 0.91011, p-value = 2.741e-10shapiro.test(rawF$PreDL)##
## Shapiro-Wilk normality test
##
## data: rawF$PreDL
## W = 0.94232, p-value = 1.118e-07shapiro.test(rawF$DbL)##
## Shapiro-Wilk normality test
##
## data: rawF$DbL
## W = 0.98676, p-value = 0.03793shapiro.test(rawF$HL)##
## Shapiro-Wilk normality test
##
## data: rawF$HL
## W = 0.87215, p-value = 1.115e-12shapiro.test(rawF$HD)##
## Shapiro-Wilk normality test
##
## data: rawF$HD
## W = 0.91737, p-value = 9.289e-10shapiro.test(rawF$HW)##
## Shapiro-Wilk normality test
##
## data: rawF$HW
## W = 0.92699, p-value = 5.211e-09shapiro.test(rawF$SnL)##
## Shapiro-Wilk normality test
##
## data: rawF$SnL
## W = 0.56341, p-value < 2.2e-16shapiro.test(rawF$OL)##
## Shapiro-Wilk normality test
##
## data: rawF$OL
## W = 0.9821, p-value = 0.006707Histograms
hist(rawF$SL)
hist(rawF$BD)
hist(rawF$CPD)
hist(rawF$CPL)
hist(rawF$PreDL)
hist(rawF$DbL)
hist(rawF$HL)
hist(rawF$HD)
hist(rawF$HW)
hist(rawF$SnL)
hist(rawF$OL)
QQ plots
qqnorm(rawF$SL)
qqline(rawF$SL)
qqnorm(rawF$BD)
qqline(rawF$BD)
qqnorm(rawF$CPD)
qqline(rawF$CPD)
qqnorm(rawF$CPL)
qqline(rawF$CPL)
qqnorm(rawF$PreDL)
qqline(rawF$PreDL)
qqnorm(rawF$DbL)
qqline(rawF$DbL)
qqnorm(rawF$HL)
qqline(rawF$HL)
qqnorm(rawF$HD)
qqline(rawF$HD)
qqnorm(rawF$HW)
qqline(rawF$HW)
qqnorm(rawF$SnL)
qqline(rawF$SnL)
qqnorm(rawF$OL)
qqline(rawF$OL)
Log transformations
Since all of the continuous characters were not normal, I will log transform them, and proceed with the transformed data in all analyses.
rawF[paste0(names(rawF), '_log')] <- log(rawF[, 25:35], 10)
#for some reason did the log of the whole data frame instead of those specific columns, so I will just remove the unnecessary columns instead of spending time on what went wrong.
rawF <- subset(rawF, select = -c(48:77) )
#double checking normality with a SW test and QQ plot
shapiro.test(rawF$SL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$SL_log
## W = 0.98318, p-value = 0.009921shapiro.test(rawF$BD_log)##
## Shapiro-Wilk normality test
##
## data: rawF$BD_log
## W = 0.95211, p-value = 1.024e-06shapiro.test(rawF$CPD_log)##
## Shapiro-Wilk normality test
##
## data: rawF$CPD_log
## W = 0.97428, p-value = 0.000465shapiro.test(rawF$CPL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$CPL_log
## W = 0.97841, p-value = 0.001827shapiro.test(rawF$PreDL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$PreDL_log
## W = 0.63075, p-value < 2.2e-16shapiro.test(rawF$DbL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$DbL_log
## W = 0.97229, p-value = 0.0002479shapiro.test(rawF$HL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$HL_log
## W = 0.97621, p-value = 0.0008721shapiro.test(rawF$HD_log)##
## Shapiro-Wilk normality test
##
## data: rawF$HD_log
## W = 0.88455, p-value = 5.875e-12shapiro.test(rawF$HW_log)##
## Shapiro-Wilk normality test
##
## data: rawF$HW_log
## W = 0.98022, p-value = 0.003423shapiro.test(rawF$SnL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$SnL_log
## W = 0.93195, p-value = 1.341e-08shapiro.test(rawF$OL_log)##
## Shapiro-Wilk normality test
##
## data: rawF$OL_log
## W = 0.98727, p-value = 0.04594qqnorm(rawF$SL_log)
qqline(rawF$SL_log)
qqnorm(rawF$BD_log)
qqline(rawF$BD_log)
qqnorm(rawF$CPD_log)
qqline(rawF$CPD_log)
qqnorm(rawF$CPL_log)
qqline(rawF$CPL_log)
qqnorm(rawF$PreDL_log)
qqline(rawF$PreDL_log)
qqnorm(rawF$DbL_log)
qqline(rawF$DbL_log)
qqnorm(rawF$HL_log)
qqline(rawF$HL_log)
qqnorm(rawF$HD_log)
qqline(rawF$HD_log)
qqnorm(rawF$HW_log)
qqline(rawF$HW_log)
qqnorm(rawF$SnL_log)
qqline(rawF$SnL_log)
qqnorm(rawF$OL_log)
qqline(rawF$OL_log)
Correcting for body size (residuals)
SL correction
Since amazons are in general bigger than sailfin, we don’t want any results to be due to this difference in body size bias. Therefore, we will see what traits are influenced by body size (regressions) and correct for body size when necessary (absolute value of residuals). We can then use the residuals when comparing between species for traits that are influenced by body size, and raw/transformed data for traits that are not influenced by body size. I will also calculate standardized residuals to compare residuals across traits in later analyses.
Quick results summary: traits not influenced by body size are left & right pelvic, anal, scales above and below lateral line (except in mexicana), scales before dorsal fin and fluctuating asymmetry; all other traits influenced by body size.
Regressions
- STEP ONE: plot trait vs body size
library(ggplot2)
library(ggpubr)
lat.F <- rawF[rawF$SPP == "p.latipinna",]
form.F <- rawF[rawF$SPP == "p.formosa",]
mex.F <- rawF[rawF$SPP == "p.mexicana",]
##### LAT #####
F.reg.lat.D <- lm(lat.F$D ~ lat.F$SL)
F.sd.lat.D <- rstandard(F.reg.lat.D)
F.reg.lat.D.plot <- ggplot(lat.F, aes(x = SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.D.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.P1 <- lm(lat.F$P1 ~ lat.F$SL)
F.sd.lat.P1 <- rstandard(F.reg.lat.P1)
F.reg.lat.P1.plot <- ggplot(lat.F, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.P1.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.P2.L <- lm(lat.F$P2.L ~ lat.F$SL)
F.sd.lat.P2.L <- rstandard(F.reg.lat.P2.L)
F.reg.lat.P2.L.plot <- ggplot(lat.F, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.P2.R <- lm(lat.F$P2.R ~ lat.F$SL)
F.sd.lat.P2.R <- rstandard(F.reg.lat.P2.R)
F.reg.lat.P2.R.plot <- ggplot(lat.F, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.A <- lm(lat.F$A ~ lat.F$SL)
F.sd.lat.A <- rstandard(F.reg.lat.A)
F.reg.lat.A.plot <- ggplot(lat.F, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.A.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.P1.R <- lm(lat.F$P1.R ~ lat.F$SL)
F.sd.lat.P1.R <- rstandard(F.reg.lat.P1.R)
F.reg.lat.P1.R.plot <- ggplot(lat.F, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.LLSC <- lm(lat.F$LLSC ~ lat.F$SL)
F.sd.lat.LLSC <- rstandard(F.reg.lat.LLSC)
F.reg.lat.LLSC.plot <- ggplot(lat.F, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.SALL <- lm(lat.F$SALL ~ lat.F$SL)
F.sd.lat.SALL <- rstandard(F.reg.lat.SALL)
F.reg.lat.SALL.plot <- ggplot(lat.F, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.SBLL <- lm(lat.F$SBLL ~ lat.F$SL)
F.sd.lat.SBLL <- rstandard(F.reg.lat.SBLL)
F.reg.lat.SBLL.plot <- ggplot(lat.F, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.SBDF <- lm(lat.F$SBDF ~ lat.F$SL)
F.sd.lat.SBDF <- rstandard(F.reg.lat.SBDF)
F.reg.lat.SBDF.plot <- ggplot(lat.F, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.BD <- lm(lat.F$BD_log ~ lat.F$SL_log)
F.sd.lat.BD <- rstandard(F.reg.lat.BD)
F.reg.lat.BD.plot <- ggplot(lat.F, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.BD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.CPD <- lm(lat.F$CPD_log ~ lat.F$SL_log)
F.sd.lat.CPD <- rstandard(F.reg.lat.CPD)
F.reg.lat.CPD.plot <- ggplot(lat.F, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.CPL <- lm(lat.F$CPL_log ~ lat.F$SL_log)
F.sd.lat.CPL <- rstandard(F.reg.lat.CPL)
F.reg.lat.CPL.plot <- ggplot(lat.F, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.PreDL <- lm(lat.F$PreDL_log ~ lat.F$SL_log)
F.sd.lat.PreDL <- rstandard(F.reg.lat.PreDL)
F.reg.lat.PreDL.plot <- ggplot(lat.F, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.DbL <- lm(lat.F$DbL_log ~ lat.F$SL_log)
F.sd.lat.DbL <- rstandard(F.reg.lat.DbL)
F.reg.lat.DbL.plot <- ggplot(lat.F, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.HL <- lm(lat.F$HL_log ~ lat.F$SL_log)
F.sd.lat.HL <- rstandard(F.reg.lat.HL)
F.reg.lat.HL.plot <- ggplot(lat.F, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.HL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.HD <- lm(lat.F$HD_log ~ lat.F$SL_log)
F.sd.lat.HD <- rstandard(F.reg.lat.HD)
F.reg.lat.HD.plot <- ggplot(lat.F, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.HD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.HW <- lm(lat.F$HW_log ~ lat.F$SL_log)
F.sd.lat.HW <- rstandard(F.reg.lat.HW)
F.reg.lat.HW.plot <- ggplot(lat.F, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.HW.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.SnL <- lm(lat.F$SnL_log ~ lat.F$SL_log)
F.sd.lat.SnL <- rstandard(F.reg.lat.SnL)
F.reg.lat.SnL.plot <- ggplot(lat.F, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.OL <- lm(lat.F$OL_log ~ lat.F$SL_log)
F.sd.lat.OL <- rstandard(F.reg.lat.OL)
F.reg.lat.OL.plot <- ggplot(lat.F, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.OL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.lat.FLA <- lm(lat.F$FLA ~ lat.F$SL)
F.sd.lat.FLA <- rstandard(F.reg.lat.FLA)
F.reg.lat.FLA.plot <- ggplot(lat.F, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.lat.FLA.plot## `geom_smooth()` using formula = 'y ~ x'
##### FORM #####
F.reg.form.D <- lm(form.F$D ~ form.F$SL)
F.sd.form.D <- rstandard(F.reg.form.D)
F.reg.form.D.plot <- ggplot(form.F, aes(x =SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.D.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.P1 <- lm(form.F$P1 ~ form.F$SL)
F.sd.form.P1 <- rstandard(F.reg.form.P1)
F.reg.form.P1.plot <- ggplot(form.F, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.P1.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.P2.L <- lm(form.F$P2.L ~ form.F$SL)
F.sd.form.P2.L <- rstandard(F.reg.form.P2.L)
F.reg.form.P2.L.plot <- ggplot(form.F, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.P2.R <- lm(form.F$P2.R ~ form.F$SL)
F.sd.form.P2.R <- rstandard(F.reg.form.P2.R)
F.reg.form.P2.R.plot <- ggplot(form.F, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.A <- lm(form.F$A ~ form.F$SL)
F.sd.form.A <- rstandard(F.reg.form.A)
F.reg.form.A.plot <- ggplot(form.F, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.A.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.P1.R <- lm(form.F$P1.R ~ form.F$SL)
F.sd.form.P1.R <- rstandard(F.reg.form.P1.R)
F.reg.form.P1.R.plot <- ggplot(form.F, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.LLSC <- lm(form.F$LLSC ~ form.F$SL)
F.sd.form.LLSC <- rstandard(F.reg.form.LLSC)
F.reg.form.LLSC.plot <- ggplot(form.F, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.SALL <- lm(form.F$SALL ~ form.F$SL)
F.sd.form.SALL <- rstandard(F.reg.form.SALL)
F.reg.form.SALL.plot <- ggplot(form.F, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.SBLL <- lm(form.F$SBLL ~ form.F$SL)
F.sd.form.SBLL <- rstandard(F.reg.form.SBLL)
F.reg.form.SBLL.plot <- ggplot(form.F, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.SBDF <- lm(form.F$SBDF ~ form.F$SL)
F.sd.form.SBDF <- rstandard(F.reg.form.SBDF)
F.reg.form.SBDF.plot <- ggplot(form.F, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.BD <- lm(form.F$BD_log ~ form.F$SL_log)
F.sd.form.BD <- rstandard(F.reg.form.BD)
F.reg.form.BD.plot <- ggplot(form.F, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.BD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.CPD <- lm(form.F$CPD_log ~ form.F$SL_log)
F.sd.form.CPD <- rstandard(F.reg.form.CPD)
F.reg.form.CPD.plot <- ggplot(form.F, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.CPL <- lm(form.F$CPL_log ~ form.F$SL_log)
F.sd.form.CPL <- rstandard(F.reg.form.CPL)
F.reg.form.CPL.plot <- ggplot(form.F, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.PreDL <- lm(form.F$PreDL_log ~ form.F$SL_log)
F.sd.form.PreDL <- rstandard(F.reg.form.PreDL)
F.reg.form.PreDL.plot <- ggplot(form.F, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.DbL <- lm(form.F$DbL_log ~ form.F$SL_log)
F.sd.form.DbL <- rstandard(F.reg.form.DbL)
F.reg.form.DbL.plot <- ggplot(form.F, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.HL <- lm(form.F$HL_log ~ form.F$SL_log)
F.sd.form.HL <- rstandard(F.reg.form.HL)
F.reg.form.HL.plot <- ggplot(form.F, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.HL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.HD <- lm(form.F$HD_log ~ form.F$SL_log)
F.sd.form.HD <- rstandard(F.reg.form.HD)
F.reg.form.HD.plot <- ggplot(form.F, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.HD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.HW <- lm(form.F$HW_log ~ form.F$SL_log)
F.sd.form.HW <- rstandard(F.reg.form.HW)
F.reg.form.HW.plot <- ggplot(form.F, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.HW.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.SnL <- lm(form.F$SnL_log ~ form.F$SL_log)
F.sd.form.SnL <- rstandard(F.reg.form.SnL)
F.reg.form.SnL.plot <- ggplot(form.F, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.OL <- lm(form.F$OL_log ~ form.F$SL_log)
F.sd.form.OL <- rstandard(F.reg.form.OL)
F.reg.form.OL.plot <- ggplot(form.F, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.OL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.form.FLA <- lm(form.F$FLA ~ form.F$SL)
F.sd.form.FLA <- rstandard(F.reg.form.FLA)
F.reg.form.FLA.plot <- ggplot(form.F, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.form.FLA.plot## `geom_smooth()` using formula = 'y ~ x'
##### MEX #####
F.reg.mex.D <- lm(mex.F$D ~ mex.F$SL)
F.sd.mex.D <- rstandard(F.reg.mex.D)
F.reg.mex.D.plot <- ggplot(mex.F, aes(x = SL, y = D)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.D.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.P1 <- lm(mex.F$P1 ~ mex.F$SL)
F.sd.mex.P1 <- rstandard(F.reg.mex.P1)
F.reg.mex.P1.plot <- ggplot(mex.F, aes(x = SL, y = P1)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.P1.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.P2.L <- lm(mex.F$P2.L ~ mex.F$SL)
F.sd.mex.P2.L <- rstandard(F.reg.mex.P2.L)
F.reg.mex.P2.L.plot <- ggplot(mex.F, aes(x = SL, y = P2.L)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.P2.L.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.P2.R <- lm(mex.F$P2.R ~ mex.F$SL)
F.sd.mex.P2.R <- rstandard(F.reg.mex.P2.R)
F.reg.mex.P2.R.plot <- ggplot(mex.F, aes(x = SL, y = P2.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.P2.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.A <- lm(mex.F$A ~ mex.F$SL)
F.sd.mex.A <- rstandard(F.reg.mex.A)
F.reg.mex.A.plot <- ggplot(mex.F, aes(x = SL, y = A)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.A.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.P1.R <- lm(mex.F$P1.R ~ mex.F$SL)
F.sd.mex.P1.R <- rstandard(F.reg.mex.P1.R)
F.reg.mex.P1.R.plot <- ggplot(mex.F, aes(x = SL, y = P1.R)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.P1.R.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.LLSC <- lm(mex.F$LLSC ~ mex.F$SL)
F.sd.mex.LLSC <- rstandard(F.reg.mex.LLSC)
F.reg.mex.LLSC.plot <- ggplot(mex.F, aes(x = SL, y = LLSC)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.LLSC.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.SALL <- lm(mex.F$SALL ~ mex.F$SL)
F.sd.mex.SALL <- rstandard(F.reg.mex.SALL)
F.reg.mex.SALL.plot <- ggplot(mex.F, aes(x = SL, y = SALL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.SALL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.SBLL <- lm(mex.F$SBLL ~ mex.F$SL)
F.sd.mex.SBLL <- rstandard(F.reg.mex.SBLL)
F.reg.mex.SBLL.plot <- ggplot(mex.F, aes(x = SL, y = SBLL)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.SBLL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.SBDF <- lm(mex.F$SBDF ~ mex.F$SL)
F.sd.mex.SBDF <- rstandard(F.reg.mex.SBDF)
F.reg.mex.SBDF.plot <- ggplot(mex.F, aes(x = SL, y = SBDF)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.SBDF.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.BD <- lm(mex.F$BD_log ~ mex.F$SL_log)
F.sd.mex.BD <- rstandard(F.reg.mex.BD)
F.reg.mex.BD.plot <- ggplot(mex.F, aes(x = SL_log, y = BD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.BD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.CPD <- lm(mex.F$CPD_log ~ mex.F$SL_log)
F.sd.mex.CPD <- rstandard(F.reg.mex.CPD)
F.reg.mex.CPD.plot <- ggplot(mex.F, aes(x = SL_log, y = CPD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.CPD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.CPL <- lm(mex.F$CPL_log ~ mex.F$SL_log)
F.sd.mex.CPL <- rstandard(F.reg.mex.CPL)
F.reg.mex.CPL.plot <- ggplot(mex.F, aes(x = SL_log, y = CPL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.CPL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.PreDL <- lm(mex.F$PreDL_log ~ mex.F$SL_log)
F.sd.mex.PreDL <- rstandard(F.reg.mex.PreDL)
F.reg.mex.PreDL.plot <- ggplot(mex.F, aes(x = SL_log, y = PreDL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.PreDL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.DbL <- lm(mex.F$DbL_log ~ mex.F$SL_log)
F.sd.mex.DbL <- rstandard(F.reg.mex.DbL)
F.reg.mex.DbL.plot <- ggplot(mex.F, aes(x = SL_log, y = DbL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.DbL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.HL <- lm(mex.F$HL_log ~ mex.F$SL_log)
F.sd.mex.HL <- rstandard(F.reg.mex.HL)
F.reg.mex.HL.plot <- ggplot(mex.F, aes(x = SL_log, y = HL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.HL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.HD <- lm(mex.F$HD_log ~ mex.F$SL_log)
F.sd.mex.HD <- rstandard(F.reg.mex.HD)
F.reg.mex.HD.plot <- ggplot(mex.F, aes(x = SL_log, y = HD_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.HD.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.HW <- lm(mex.F$HW_log ~ mex.F$SL_log)
F.sd.mex.HW <- rstandard(F.reg.mex.HW)
F.reg.mex.HW.plot <- ggplot(mex.F, aes(x = SL_log, y = HW_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.HW.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.SnL <- lm(mex.F$SnL_log ~ mex.F$SL_log)
F.sd.mex.SnL <- rstandard(F.reg.mex.SnL)
F.reg.mex.SnL.plot <- ggplot(mex.F, aes(x = SL_log, y = SnL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.SnL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.OL <- lm(mex.F$OL_log ~ mex.F$SL_log)
F.sd.mex.OL <- rstandard(F.reg.mex.OL)
F.reg.mex.OL.plot <- ggplot(mex.F, aes(x = SL_log, y = OL_log)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.OL.plot## `geom_smooth()` using formula = 'y ~ x'
F.reg.mex.FLA <- lm(mex.F$FLA ~ mex.F$SL)
F.sd.mex.FLA <- rstandard(F.reg.mex.FLA)
F.reg.mex.FLA.plot <- ggplot(mex.F, aes(x = SL, y = FLA)) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
stat_cor(label.y = 10)
F.reg.mex.FLA.plot## `geom_smooth()` using formula = 'y ~ x'